bionet@0.0.1

Vulnerabilities

20 via 62 paths

Dependencies

495

Source

npm

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Severity
  • 8
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Status
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high severity

Prototype Pollution

  • Vulnerable module: ajv
  • Introduced through: less@2.7.3

Detailed paths

  • Introduced through: bionet@0.0.1 less@2.7.3 request@2.81.0 har-validator@4.2.1 ajv@4.11.8
    Remediation: Upgrade to less@3.0.2.

Overview

ajv is an Another JSON Schema Validator

Affected versions of this package are vulnerable to Prototype Pollution. A carefully crafted JSON schema could be provided that allows execution of other code by prototype pollution. (While untrusted schemas are recommended against, the worst case of an untrusted schema should be a denial of service, not execution of code.)

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

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 ajv to version 6.12.3 or higher.

References

high severity
new

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: ansi-regex
  • Introduced through: level@1.7.0

Detailed paths

  • Introduced through: bionet@0.0.1 level@1.7.0 leveldown@1.7.2 prebuild-install@2.5.3 npmlog@4.1.2 gauge@2.7.4 strip-ansi@3.0.1 ansi-regex@2.1.1
  • Introduced through: bionet@0.0.1 level@1.7.0 leveldown@1.7.2 prebuild-install@2.5.3 npmlog@4.1.2 gauge@2.7.4 string-width@1.0.2 strip-ansi@3.0.1 ansi-regex@2.1.1
  • Introduced through: bionet@0.0.1 level@1.7.0 leveldown@1.7.2 prebuild-install@2.5.3 npmlog@4.1.2 gauge@2.7.4 wide-align@1.1.3 string-width@2.1.1 strip-ansi@4.0.0 ansi-regex@3.0.0

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 6.0.1, 5.0.1 or higher.

References

high severity

Remote Memory Exposure

  • Vulnerable module: bl
  • Introduced through: accountdown@4.1.0 and cookie-auth@2.4.2

Detailed paths

  • Introduced through: bionet@0.0.1 accountdown@4.1.0 levelup-defaults@1.0.2 levelup@0.19.1 bl@0.8.2
  • Introduced through: bionet@0.0.1 cookie-auth@2.4.2 memdb@0.2.0 levelup@0.18.6 bl@0.8.2

Overview

bl is a library that allows you to collect buffers and access with a standard readable buffer interface.

Affected versions of this package are vulnerable to Remote Memory Exposure. If user input ends up in consume() argument and can become negative, BufferList state can be corrupted, tricking it into exposing uninitialized memory via regular .slice() calls.

PoC by chalker

const { BufferList } = require('bl')
const secret = require('crypto').randomBytes(256)
for (let i = 0; i < 1e6; i++) {
  const clone = Buffer.from(secret)
  const bl = new BufferList()
  bl.append(Buffer.from('a'))
  bl.consume(-1024)
  const buf = bl.slice(1)
  if (buf.indexOf(clone) !== -1) {
    console.error(`Match (at ${i})`, buf)
  }
}

Remediation

Upgrade bl to version 2.2.1, 3.0.1, 4.0.3, 1.2.3 or higher.

References

high severity

Denial of Service (DoS)

  • Vulnerable module: ecstatic
  • Introduced through: ecstatic@0.8.0

Detailed paths

  • Introduced through: bionet@0.0.1 ecstatic@0.8.0
    Remediation: Upgrade to ecstatic@4.1.4.

Overview

ecstatic is a simple static file server middleware. Use it with a raw http server, express/connect or on the CLI.

Affected versions of this package are vulnerable to Denial of Service (DoS). It is possible to crash a server using the package due to the way URL params parsing is handled during redirect.

PoC

curl --path-as-is $(echo -e -n "http://127.0.0.1:8080/existing-dir-name?\x0cfoo")

In the PoC the library is trying to redirect /existing-dir-name?\x0cfoo to /existing-dir-name/?\x0cfoo which cause TypeError: The header content contains invalid characters error because of \x0c symbol.

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 ecstatic to version 4.1.4 or higher.

References

high severity

Denial of Service (DoS)

  • Vulnerable module: ecstatic
  • Introduced through: ecstatic@0.8.0

Detailed paths

  • Introduced through: bionet@0.0.1 ecstatic@0.8.0
    Remediation: Upgrade to ecstatic@1.4.0.

Overview

ecstatic is a simple static file server middleware. Use it with a raw http server, express/connect or on the CLI.

Affected versions of this package are vulnerable to Denial of Service (DoS). The vulnerability is caused by the combination of two bugs. First, the underlying V8 engine throws an exception when processing the specially crafted date, instead of stating the date is invalid as it should. Second, the ecstatic server does not handle the exception, triggering the crash.

Upgrading Ecstatic will address the second issue and thus fix the vulnerability.

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 ecstatic to version 1.4.0 or higher.

References

high severity

Command Injection

  • Vulnerable module: nodemailer
  • Introduced through: nodemailer@2.7.2

Detailed paths

  • Introduced through: bionet@0.0.1 nodemailer@2.7.2
    Remediation: Upgrade to nodemailer@6.4.16.

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
new

Prototype Pollution

  • Vulnerable module: set-value
  • Introduced through: watchify@3.11.1

Detailed paths

  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 braces@2.3.2 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 anymatch@2.0.0 micromatch@3.1.10 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 braces@2.3.2 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 anymatch@2.0.0 micromatch@3.1.10 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 anymatch@2.0.0 micromatch@3.1.10 braces@2.3.2 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 anymatch@2.0.0 micromatch@3.1.10 extglob@2.0.4 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 anymatch@2.0.0 micromatch@3.1.10 nanomatch@1.2.13 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 anymatch@2.0.0 micromatch@3.1.10 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 readdirp@2.2.1 micromatch@3.1.10 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 anymatch@2.0.0 micromatch@3.1.10 braces@2.3.2 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 anymatch@2.0.0 micromatch@3.1.10 extglob@2.0.4 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 anymatch@2.0.0 micromatch@3.1.10 nanomatch@1.2.13 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 anymatch@2.0.0 micromatch@3.1.10 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 readdirp@2.2.1 micromatch@3.1.10 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 anymatch@2.0.0 micromatch@3.1.10 braces@2.3.2 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 readdirp@2.2.1 micromatch@3.1.10 braces@2.3.2 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 anymatch@2.0.0 micromatch@3.1.10 extglob@2.0.4 expand-brackets@2.1.4 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 anymatch@2.0.0 micromatch@3.1.10 extglob@2.0.4 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 readdirp@2.2.1 micromatch@3.1.10 extglob@2.0.4 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 anymatch@2.0.0 micromatch@3.1.10 nanomatch@1.2.13 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 readdirp@2.2.1 micromatch@3.1.10 nanomatch@1.2.13 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 anymatch@2.0.0 micromatch@3.1.10 braces@2.3.2 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 readdirp@2.2.1 micromatch@3.1.10 braces@2.3.2 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 anymatch@2.0.0 micromatch@3.1.10 extglob@2.0.4 expand-brackets@2.1.4 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 anymatch@2.0.0 micromatch@3.1.10 extglob@2.0.4 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 readdirp@2.2.1 micromatch@3.1.10 extglob@2.0.4 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 anymatch@2.0.0 micromatch@3.1.10 nanomatch@1.2.13 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 readdirp@2.2.1 micromatch@3.1.10 nanomatch@1.2.13 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 anymatch@2.0.0 micromatch@3.1.10 extglob@2.0.4 expand-brackets@2.1.4 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 readdirp@2.2.1 micromatch@3.1.10 extglob@2.0.4 expand-brackets@2.1.4 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 anymatch@2.0.0 micromatch@3.1.10 extglob@2.0.4 expand-brackets@2.1.4 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1
  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 readdirp@2.2.1 micromatch@3.1.10 extglob@2.0.4 expand-brackets@2.1.4 snapdragon@0.8.2 base@0.11.2 cache-base@1.0.1 union-value@1.0.1 set-value@2.0.1

Overview

set-value is a package that creates nested values and any intermediaries using dot notation ('a.b.c') paths.

Affected versions of this package are vulnerable to Prototype Pollution. A type confusion vulnerability can lead to a bypass of CVE-2019-10747 when the user-provided keys used in the path parameter are arrays.

PoC

const set = require("set-value")

// set({}, ['__proto__','polluted'], 'yes');
// console.log(polluted); // Error: Cannot set unsafe key: "__proto__"

set({}, [['__proto__'],'polluted'], 'yes');
console.log(polluted);

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

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 set-value to version 4.0.1 or higher.

References

high severity
new

Command Injection

  • Vulnerable module: ssh2
  • Introduced through: ssh2@0.5.5

Detailed paths

  • Introduced through: bionet@0.0.1 ssh2@0.5.5
    Remediation: Upgrade to ssh2@1.0.0.

Overview

ssh2 is a SSH2 client and server modules written in pure JavaScript for node.js.

Affected versions of this package are vulnerable to Command Injection. The issue only exists on Windows. This issue may lead to remote code execution if a client of the library calls the vulnerable method with untrusted input.

Remediation

Upgrade ssh2 to version 1.0.0 or higher.

References

medium severity

Uninitialized Memory Exposure

  • Vulnerable module: bl
  • Introduced through: accountdown@4.1.0 and cookie-auth@2.4.2

Detailed paths

  • Introduced through: bionet@0.0.1 accountdown@4.1.0 levelup-defaults@1.0.2 levelup@0.19.1 bl@0.8.2
  • Introduced through: bionet@0.0.1 cookie-auth@2.4.2 memdb@0.2.0 levelup@0.18.6 bl@0.8.2

Overview

bl is a storage object for collections of Node Buffers.

A possible memory disclosure vulnerability exists when a value of type number is provided to the append() method and results in concatenation of uninitialized memory to the buffer collection.

This is a result of unobstructed use of the Buffer constructor, whose insecure default constructor increases the odds of memory leakage.

Details

Constructing a Buffer class with integer N creates a Buffer of length N with raw (not "zero-ed") memory.

In the following example, the first call would allocate 100 bytes of memory, while the second example will allocate the memory needed for the string "100":

// uninitialized Buffer of length 100
x = new Buffer(100);
// initialized Buffer with value of '100'
x = new Buffer('100');

bl's append function uses the default Buffer constructor as-is, making it easy to append uninitialized memory to an existing list. If the value of the buffer list is exposed to users, it may expose raw server side memory, potentially holding secrets, private data and code. This is a similar vulnerability to the infamous Heartbleed flaw in OpenSSL.

const BufferList = require('bl')

var bl = new BufferList()
bl.append(new Buffer('abcd'))
bl.append(new Buffer('efg'))
bl.append('100')
// appends a Buffer holding 100 bytes of uninitialized memory
bl.append(100)                     
bl.append(new Buffer('j'))

You can read more about the insecure Buffer behavior on our blog.

Similar vulnerabilities were discovered in request, mongoose, ws and sequelize.

Note This is vulnerable only for Node <=4

References

medium severity

Denial of Service (DoS)

  • Vulnerable module: ecstatic
  • Introduced through: ecstatic@0.8.0

Detailed paths

  • Introduced through: bionet@0.0.1 ecstatic@0.8.0
    Remediation: Upgrade to ecstatic@2.0.0.

Overview

ecstatic is a simple static file server middleware. Use it with a raw http server, express/connect or on the CLI.

Affected versions of this package are vulnerable to Denial of Service (DoS). The process of replacing null bytes in the url string is being done in a loop:

Find Null Bytes --> If found remove Null Byte --> Repeat

When no more Null Bytes found, the flow of the program continues.

This method would work fine with a normal URL that should be relatively short, but a malicious user may craft a very long URL with a lot of Null Bytes.

PoC by Checkmarx:

http://www.checkmarx.com/advisories/%00%00%00%00%00%00...

Slowdown:

A payload of 22kB caused a lag of 1 second, A payload of 35kB caused a lag of 3 seconds, A payload of 86kB caused the server to crash

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 ecstatic to version 2.0.0 or higher.

References

medium severity

Open Redirect

  • Vulnerable module: ecstatic
  • Introduced through: ecstatic@0.8.0

Detailed paths

  • Introduced through: bionet@0.0.1 ecstatic@0.8.0
    Remediation: Upgrade to ecstatic@2.2.2.

Overview

ecstatic is a simple static file server middleware. Use it with a raw http server, express/connect or on the CLI.

Affected versions of this package are vulnerable to Open Redirect. The package failed to validate redirects, allowing attackers to craft requests that result in an HTTP 301 redirect to any other domains.

Remediation

Upgrade ecstatic to version 2.2.2, 3.3.2, 4.1.2 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: glob-parent
  • Introduced through: watchify@3.11.1

Detailed paths

  • Introduced through: bionet@0.0.1 watchify@3.11.1 chokidar@2.1.8 glob-parent@3.1.0
    Remediation: Upgrade to watchify@4.0.0.

Overview

glob-parent is a package that helps extracting the non-magic parent path from a glob string.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). The enclosure regex used to check for strings ending in enclosure containing path separator.

PoC by Yeting Li

var globParent = require("glob-parent")
function build_attack(n) {
var ret = "{"
for (var i = 0; i < n; i++) {
ret += "/"
}

return ret;
}

globParent(build_attack(5000));

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 glob-parent to version 5.1.2 or higher.

References

medium severity

Prototype Pollution

  • Vulnerable module: hoek
  • Introduced through: less@2.7.3

Detailed paths

  • Introduced through: bionet@0.0.1 less@2.7.3 request@2.81.0 hawk@3.1.3 hoek@2.16.3
    Remediation: Upgrade to less@3.0.2.
  • Introduced through: bionet@0.0.1 less@2.7.3 request@2.81.0 hawk@3.1.3 boom@2.10.1 hoek@2.16.3
    Remediation: Upgrade to less@3.0.2.
  • Introduced through: bionet@0.0.1 less@2.7.3 request@2.81.0 hawk@3.1.3 sntp@1.0.9 hoek@2.16.3
    Remediation: Upgrade to less@3.0.2.
  • Introduced through: bionet@0.0.1 less@2.7.3 request@2.81.0 hawk@3.1.3 cryptiles@2.0.5 boom@2.10.1 hoek@2.16.3
    Remediation: Upgrade to less@3.0.2.

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

Insecure Randomness

  • Vulnerable module: node-uuid
  • Introduced through: shoe@0.0.15

Detailed paths

  • Introduced through: bionet@0.0.1 shoe@0.0.15 sockjs@0.3.7 node-uuid@1.3.3

Overview

node-uuid is a Simple, fast generation of RFC4122 UUIDS.

Affected versions of this package are vulnerable to Insecure Randomness. It uses the cryptographically insecure Math.random which can produce predictable values and should not be used in security-sensitive context.

Remediation

Upgrade node-uuid to version 1.4.4 or greater.

References

medium severity

HTTP Header Injection

  • Vulnerable module: nodemailer
  • Introduced through: nodemailer@2.7.2

Detailed paths

  • Introduced through: bionet@0.0.1 nodemailer@2.7.2
    Remediation: Upgrade to nodemailer@6.6.1.

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

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: semver
  • Introduced through: cookie-auth@2.4.2

Detailed paths

  • Introduced through: bionet@0.0.1 cookie-auth@2.4.2 memdb@0.2.0 levelup@0.18.6 semver@2.3.2
    Remediation: Open PR to patch semver@2.3.2.

Overview

semver is a semantic version parser used by npm.

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

Overview

npm is a package manager for javascript.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). The semver module uses regular expressions when parsing a version string. For a carefully crafted input, the time it takes to process these regular expressions is not linear to the length of the input. Since the semver module did not enforce a limit on the version string length, an attacker could provide a long string that would take up a large amount of resources, potentially taking a server down. This issue therefore enables a potential Denial of Service attack. This is a slightly differnt variant of a typical Regular Expression Denial of Service (ReDoS) vulnerability.

Details

<>

Remediation

Update to a version 4.3.2 or greater. From the issue description [2]: "Package version can no longer be more than 256 characters long. This prevents a situation in which parsing the version number can use exponentially more time and memory to parse, leading to a potential denial of service."

References

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 4.3.2 or higher.

References

medium severity

Denial of Service (DoS)

  • Vulnerable module: sockjs
  • Introduced through: shoe@0.0.15

Detailed paths

  • Introduced through: bionet@0.0.1 shoe@0.0.15 sockjs@0.3.7

Overview

sockjs is a JavaScript library (for browsers) that provides a WebSocket-like object.

Affected versions of this package are vulnerable to Denial of Service (DoS). Incorrect handling of Upgrade header with the value websocket leads in crashing of containers hosting sockjs apps.

PoC by Andrew Snow

import requests
import random
import argparse

def main():
  print('SockJS 0.3.19 Denial of Service POC')
  print('For educational purposes only')
  print('Author: @andsnw')
  print('------------')
  parser = argparse.ArgumentParser(description='SockJS 0.3.19 Denial of Service POC')
  parser.add_argument('--target', type=str, help='URL of target running vulnerable sockjs')
  parsed = parser.parse_args()
  target = vars(parsed)['target']
  if target == None:
    parser.print_help()
    exit()

  # Clean trailing /
  if target.endswith('/'):
    target = target[:-1]

  print ("Initiating at: %s" % target)

  # Create sockjs payload
  payloads = [
    ('%s/sockjs/' % target),
    ('%s/sockjs/598/' % target),
    ('%s/sockjs/598/8ko8gkpf/' % target),
  ]

  # Run 3 times with traversion
  for url in payloads:
    payload_url = "%s%s" % (url, random.randint(1000000000000000000,9999999999999999999))
    print('Requesting: %s' % payload_url)
    req = requests.get(url=payload_url, headers={
      'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64; rv:68.0) Gecko/20100101 Firefox/68.0',
      'Cache-Control': 'max-age=0',
      'Accept-Language': 'en-US,en;q=0.5',
      'Connection': 'Upgrade',
      'Upgrade': 'websocket',
    })
    print("Status code: %s" % req.status_code)

  print ("Complete! Check if the container has crashed")

if __name__ == "__main__":
    main()

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 sockjs to version 0.3.20 or higher.

References

medium severity

Arbitrary Code Injection

  • Vulnerable module: underscore
  • Introduced through: nodemailer-smtp-transport@2.7.4 and nodemailer@2.7.2

Detailed paths

  • Introduced through: bionet@0.0.1 nodemailer-smtp-transport@2.7.4 smtp-connection@2.12.0 httpntlm@1.6.1 underscore@1.7.0
  • Introduced through: bionet@0.0.1 nodemailer@2.7.2 nodemailer-direct-transport@3.3.2 smtp-connection@2.12.0 httpntlm@1.6.1 underscore@1.7.0
  • Introduced through: bionet@0.0.1 nodemailer@2.7.2 nodemailer-smtp-pool@2.8.2 smtp-connection@2.12.0 httpntlm@1.6.1 underscore@1.7.0
  • Introduced through: bionet@0.0.1 nodemailer@2.7.2 nodemailer-smtp-transport@2.7.2 smtp-connection@2.12.0 httpntlm@1.6.1 underscore@1.7.0

Overview

underscore is a JavaScript's functional programming helper library.

Affected versions of this package are vulnerable to Arbitrary Code Injection via the template function, particularly when the variable option is taken from _.templateSettings as it is not sanitized.

PoC

const _ = require('underscore');
_.templateSettings.variable = "a = this.process.mainModule.require('child_process').execSync('touch HELLO')";
const t = _.template("")();

Remediation

Upgrade underscore to version 1.13.0-2, 1.12.1 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: ws
  • Introduced through: websocket-stream@3.3.3

Detailed paths

  • Introduced through: bionet@0.0.1 websocket-stream@3.3.3 ws@1.1.5
    Remediation: Upgrade to websocket-stream@5.2.0.

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

low severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: ms
  • Introduced through: jsonwebtoken@5.7.0 and rpc-multiauth@0.0.1

Detailed paths

  • Introduced through: bionet@0.0.1 jsonwebtoken@5.7.0 ms@0.7.3
    Remediation: Upgrade to jsonwebtoken@7.4.1.
  • Introduced through: bionet@0.0.1 rpc-multiauth@0.0.1 jsonwebtoken@5.7.0 ms@0.7.3
    Remediation: Open PR to patch ms@0.7.3.

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