Find, fix and prevent vulnerabilities in your code.
critical severity
- Vulnerable module: constantinople
- Introduced through: jade@1.11.0
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › jade@1.11.0 › constantinople@3.0.2
Overview
constantinople is a Determine whether a JavaScript expression evaluates to a constant (using acorn)
Affected versions of this package are vulnerable to Sandbox Bypass which can lead to arbitrary code execution.
Remediation
Upgrade constantinople
to version 3.1.1 or higher.
References
critical severity
- Vulnerable module: socket.io-parser
- Introduced through: socket.io@1.7.4
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-parser@2.3.1Remediation: Upgrade to socket.io@2.2.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-adapter@0.5.0 › socket.io-parser@2.3.1
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › socket.io-parser@2.3.1Remediation: Upgrade to socket.io@2.2.0.
Overview
socket.io-parser is a socket.io protocol parser
Affected versions of this package are vulnerable to Improper Input Validation.
when parsing attachments containing untrusted user input. Attackers can overwrite the _placeholder
object to place references to functions in query objects.
PoC
const decoder = new Decoder();
decoder.on("decoded", (packet) => {
console.log(packet.data); // prints [ 'hello', [Function: splice] ]
})
decoder.add('51-["hello",{"_placeholder":true,"num":"splice"}]');
decoder.add(Buffer.from("world"));
Remediation
Upgrade socket.io-parser
to version 3.3.3, 3.4.2, 4.0.5, 4.2.1 or higher.
References
high severity
- Vulnerable module: uglify-js
- Introduced through: jade@1.11.0
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › jade@1.11.0 › transformers@2.1.0 › uglify-js@2.2.5Remediation: Open PR to patch uglify-js@2.2.5.
Overview
uglify-js
is a JavaScript parser, minifier, compressor and beautifier toolkit.
Tom MacWright discovered that UglifyJS versions 2.4.23 and earlier are affected by a vulnerability which allows a specially crafted Javascript file to have altered functionality after minification. This bug was demonstrated by Yan to allow potentially malicious code to be hidden within secure code, activated by minification.
Details
In Boolean algebra, DeMorgan's laws describe the relationships between conjunctions (&&
), disjunctions (||
) and negations (!
).
In Javascript form, they state that:
!(a && b) === (!a) || (!b)
!(a || b) === (!a) && (!b)
The law does not hold true when one of the values is not a boolean however.
Vulnerable versions of UglifyJS do not account for this restriction, and erroneously apply the laws to a statement if it can be reduced in length by it.
Consider this authentication function:
function isTokenValid(user) {
var timeLeft =
!!config && // config object exists
!!user.token && // user object has a token
!user.token.invalidated && // token is not explicitly invalidated
!config.uninitialized && // config is initialized
!config.ignoreTimestamps && // don't ignore timestamps
getTimeLeft(user.token.expiry); // > 0 if expiration is in the future
// The token must not be expired
return timeLeft > 0;
}
function getTimeLeft(expiry) {
return expiry - getSystemTime();
}
When minified with a vulnerable version of UglifyJS, it will produce the following insecure output, where a token will never expire:
( Formatted for readability )
function isTokenValid(user) {
var timeLeft = !( // negation
!config // config object does not exist
|| !user.token // user object does not have a token
|| user.token.invalidated // token is explicitly invalidated
|| config.uninitialized // config isn't initialized
|| config.ignoreTimestamps // ignore timestamps
|| !getTimeLeft(user.token.expiry) // > 0 if expiration is in the future
);
return timeLeft > 0
}
function getTimeLeft(expiry) {
return expiry - getSystemTime()
}
Remediation
Upgrade UglifyJS to version 2.4.24
or higher.
References
high severity
- Vulnerable module: ajv
- Introduced through: watson-developer-cloud@3.18.4
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › watson-developer-cloud@3.18.4 › request@2.87.0 › har-validator@5.0.3 › ajv@5.5.2Remediation: Upgrade to watson-developer-cloud@4.0.1.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › watson-developer-cloud@3.18.4 › ibm-cloud-sdk-core@0.0.3 › request@2.87.0 › har-validator@5.0.3 › ajv@5.5.2Remediation: Upgrade to watson-developer-cloud@4.0.0.
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 mergeProperty 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
Freeze the prototype— use
Object.freeze (Object.prototype)
.Require schema validation of JSON input.
Avoid using unsafe recursive merge functions.
Consider using objects without prototypes (for example,
Object.create(null)
), breaking the prototype chain and preventing pollution.As a best practice use
Map
instead ofObject
.
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
- Vulnerable module: engine.io
- Introduced through: socket.io@1.7.4
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › engine.io@1.8.5Remediation: Upgrade to socket.io@3.0.0.
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 4.0.0 or higher.
References
high severity
- Vulnerable module: engine.io
- Introduced through: socket.io@1.7.4
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › engine.io@1.8.5Remediation: Upgrade to socket.io@2.5.0.
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
- Vulnerable module: parsejson
- Introduced through: socket.io@1.7.4
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › engine.io-client@1.8.6 › parsejson@0.0.3
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › engine.io-client@1.8.6 › parsejson@0.0.3
Overview
parsejson is a method that parses a JSON string and returns a JSON object.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) attacks. An attacker may pass a specially crafted JSON data, causing the server to hang.
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:
- CCC
- CC+C
- C+CC
- 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
There is no fixed version for parsejson
.
References
high severity
- Vulnerable module: socket.io-parser
- Introduced through: socket.io@1.7.4
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-parser@2.3.1Remediation: Upgrade to socket.io@2.2.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-adapter@0.5.0 › socket.io-parser@2.3.1
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › socket.io-parser@2.3.1Remediation: Upgrade to socket.io@2.2.0.
Overview
socket.io-parser is a socket.io protocol parser
Affected versions of this package are vulnerable to Denial of Service (DoS) via a large packet because a concatenation approach is used.
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 socket.io-parser
to version 3.3.2, 3.4.1 or higher.
References
medium severity
- Vulnerable module: request
- Introduced through: fetch-tweets@0.1.7, haven-entity-extraction@git://github.com/Lissy93/haven-entity-extraction.git and others
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › fetch-tweets@0.1.7 › request@2.88.2
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › haven-entity-extraction@git://github.com/Lissy93/haven-entity-extraction.git › request@2.88.2
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › haven-sentiment-analysis@git://github.com/Lissy93/haven-sentiment-analysis.git › request@2.88.2
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › place-lookup@1.0.1 › request@2.88.2
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › stream-tweets@1.1.0 › request@2.88.2
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › twit@2.2.11 › request@2.88.2
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › watson-developer-cloud@3.18.4 › request@2.87.0
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › watson-developer-cloud@3.18.4 › ibm-cloud-sdk-core@0.0.3 › request@2.87.0
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
- Vulnerable module: tough-cookie
- Introduced through: fetch-tweets@0.1.7, haven-entity-extraction@git://github.com/Lissy93/haven-entity-extraction.git and others
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › fetch-tweets@0.1.7 › request@2.88.2 › tough-cookie@2.5.0
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › haven-entity-extraction@git://github.com/Lissy93/haven-entity-extraction.git › request@2.88.2 › tough-cookie@2.5.0
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › haven-sentiment-analysis@git://github.com/Lissy93/haven-sentiment-analysis.git › request@2.88.2 › tough-cookie@2.5.0
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › place-lookup@1.0.1 › request@2.88.2 › tough-cookie@2.5.0
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › stream-tweets@1.1.0 › request@2.88.2 › tough-cookie@2.5.0
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › twit@2.2.11 › request@2.88.2 › tough-cookie@2.5.0
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › watson-developer-cloud@3.18.4 › request@2.87.0 › tough-cookie@2.3.4
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › watson-developer-cloud@3.18.4 › ibm-cloud-sdk-core@0.0.3 › request@2.87.0 › tough-cookie@2.3.4
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 mergeProperty 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
Freeze the prototype— use
Object.freeze (Object.prototype)
.Require schema validation of JSON input.
Avoid using unsafe recursive merge functions.
Consider using objects without prototypes (for example,
Object.create(null)
), breaking the prototype chain and preventing pollution.As a best practice use
Map
instead ofObject
.
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
- Vulnerable module: socket.io
- Introduced through: socket.io@1.7.4
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4Remediation: Upgrade to socket.io@2.4.0.
Overview
socket.io is a node.js realtime framework server.
Affected versions of this package are vulnerable to Insecure Defaults due to CORS Misconfiguration. All domains are whitelisted by default.
Remediation
Upgrade socket.io
to version 2.4.0 or higher.
References
medium severity
- Vulnerable module: uglify-js
- Introduced through: jade@1.11.0
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › jade@1.11.0 › transformers@2.1.0 › uglify-js@2.2.5
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › jade@1.11.0 › uglify-js@2.8.29
Overview
uglify-js is a JavaScript parser, minifier, compressor and beautifier toolkit.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the string_template
and the decode_template
functions.
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:
- CCC
- CC+C
- C+CC
- 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 uglify-js
to version 3.14.3 or higher.
References
medium severity
- Vulnerable module: uglify-js
- Introduced through: jade@1.11.0
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › jade@1.11.0 › transformers@2.1.0 › uglify-js@2.2.5Remediation: Open PR to patch uglify-js@2.2.5.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › jade@1.11.0 › transformers@2.1.0 › uglify-js@2.2.5Remediation: Open PR to patch uglify-js@2.2.5.
Overview
The parse()
function in the uglify-js
package prior to version 2.6.0 is vulnerable to regular expression denial of service (ReDoS) attacks when long inputs of certain patterns are processed.
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:
- CCC
- CC+C
- C+CC
- 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 to version 2.6.0
or greater.
If a direct dependency update is not possible, use snyk wizard
to patch this vulnerability.
References
medium severity
- Vulnerable module: ws
- Introduced through: socket.io@1.7.4
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › engine.io@1.8.5 › ws@1.1.5Remediation: Upgrade to socket.io@2.3.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › engine.io-client@1.8.6 › ws@1.1.5Remediation: Upgrade to socket.io@2.4.0.
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:
- CCC
- CC+C
- C+CC
- 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
- Vulnerable module: clean-css
- Introduced through: jade@1.11.0
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › jade@1.11.0 › clean-css@3.4.28
Overview
clean-css is a fast and efficient CSS optimizer for Node.js platform and any modern browser.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). attacks. 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 20th, 2018 - Initial Response from package owner
- Mar 6th, 2018 - Fix issued
- Mar 7th, 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:
- CCC
- CC+C
- C+CC
- 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 clean-css
to version 4.1.11 or higher.
References
low severity
- Vulnerable module: debug
- Introduced through: socket.io@1.7.4
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › debug@2.3.3Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › engine.io@1.8.5 › debug@2.3.3Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-adapter@0.5.0 › debug@2.3.3Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › debug@2.3.3Remediation: Upgrade to socket.io@2.0.2.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › engine.io-client@1.8.6 › debug@2.3.3Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-parser@2.3.1 › debug@2.2.0Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-adapter@0.5.0 › socket.io-parser@2.3.1 › debug@2.2.0Remediation: Open PR to patch debug@2.2.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › socket.io-parser@2.3.1 › debug@2.2.0Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › debug@2.3.3Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › engine.io@1.8.5 › debug@2.3.3Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-adapter@0.5.0 › debug@2.3.3Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › debug@2.3.3Remediation: Upgrade to socket.io@2.0.2.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › engine.io-client@1.8.6 › debug@2.3.3Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-parser@2.3.1 › debug@2.2.0Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-adapter@0.5.0 › socket.io-parser@2.3.1 › debug@2.2.0Remediation: Open PR to patch debug@2.2.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › socket.io-parser@2.3.1 › debug@2.2.0Remediation: Upgrade to socket.io@2.0.0.
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:
- CCC
- CC+C
- C+CC
- 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
- Vulnerable module: ms
- Introduced through: socket.io@1.7.4
Detailed paths
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › debug@2.3.3 › ms@0.7.2Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › engine.io@1.8.5 › debug@2.3.3 › ms@0.7.2Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-adapter@0.5.0 › debug@2.3.3 › ms@0.7.2Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › debug@2.3.3 › ms@0.7.2Remediation: Upgrade to socket.io@2.0.2.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › engine.io-client@1.8.6 › debug@2.3.3 › ms@0.7.2Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-parser@2.3.1 › debug@2.2.0 › ms@0.7.1Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-adapter@0.5.0 › socket.io-parser@2.3.1 › debug@2.2.0 › ms@0.7.1Remediation: Open PR to patch ms@0.7.1.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › socket.io-parser@2.3.1 › debug@2.2.0 › ms@0.7.1Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › debug@2.3.3 › ms@0.7.2Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › engine.io@1.8.5 › debug@2.3.3 › ms@0.7.2Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-adapter@0.5.0 › debug@2.3.3 › ms@0.7.2Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › debug@2.3.3 › ms@0.7.2Remediation: Upgrade to socket.io@2.0.2.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › engine.io-client@1.8.6 › debug@2.3.3 › ms@0.7.2Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-parser@2.3.1 › debug@2.2.0 › ms@0.7.1Remediation: Upgrade to socket.io@2.0.0.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-adapter@0.5.0 › socket.io-parser@2.3.1 › debug@2.2.0 › ms@0.7.1Remediation: Open PR to patch ms@0.7.1.
-
Introduced through: twitter-sentiment-visualisation@Lissy93/Twitter-Sentiment-Visualisation#e9862a1e2aa6b2251da4110a2cb74d0697706fd1 › socket.io@1.7.4 › socket.io-client@1.7.4 › socket.io-parser@2.3.1 › debug@2.2.0 › ms@0.7.1Remediation: Upgrade to socket.io@2.0.0.
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:
- CCC
- CC+C
- C+CC
- 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.