Snyk test report

Out-of-bounds Write

---

NVD Description

Note: Versions mentioned in the description apply only to the upstream zlib package and not the zlib package as distributed by Alpine. See How to fix? for Alpine:3.23 relevant fixed versions and status.

zlib versions 1.3.1.2 through 1.3.2 contain a heap buffer overflow vulnerability in the gz_vacate() function when processing non-blocking gzwrite() operations with stale external buffer pointers. Attackers can trigger the overflow by calling gzprintf() or gzvprintf() after a write stall, causing an unchecked memmove() to write beyond the internal input buffer boundary.

Remediation

Upgrade Alpine:3.23 zlib to version 1.3.2-r1 or higher.

References

• https://gist.github.com/thesmartshadow/e0b9481792afb7c31e86fee1ff084490
• https://github.com/madler/zlib
• https://github.com/madler/zlib/blob/v1.3.2/gzwrite.c#L393
• https://www.vulncheck.com/advisories/zlib-1.3.1.2-through-1.3.2-heap-buffer-overflow-via-gz-vacate

---

Asymmetric Resource Consumption (Amplification)

---

Overview

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

Affected versions of this package are vulnerable to Asymmetric Resource Consumption (Amplification) when handling a large number of very small fragments and data chunks. An attacker can cause excessive memory allocation and OOM by sending a high volume of tiny WebSocket frames

Workaround

This vulnerability can be mitigated by lowering the value of the maxPayload option.

PoC

import { WebSocket, WebSocketServer } from 'ws';
        
        const wss = new WebSocketServer({ port: 0 }, function () {
          const data = Buffer.alloc(1);
          const options = { fin: false };
          const { port } = wss.address();
          const ws = new WebSocket(`ws://localhost:${port}`);
        
          ws.on('open', function () {
            (function send() {
              ws.send(data, options, function (err) {
                if (err) return;
                send();
              });
            })();
          });
        
          ws.on('error', console.error);
          ws.on('close', function (code, reason) {
            console.log(`client close - code: ${code} reason: ${reason.toString()}`);
          });
        });
        
        wss.on('connection', function (ws) {
          ws.on('error', console.error);
          ws.on('close', function (code, reason) {
            console.log(`server close - code: ${code} reason: ${reason.toString()}`);
          });
        });
        

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 ws to version 5.2.5, 6.2.4, 7.5.11, 8.21.0 or higher.

References

• GitHub Advisory
• GitHub Commit
• Red Hat Bugzilla Bug

---

Symlink Attack

---

Overview

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

Affected versions of this package are vulnerable to Symlink Attack exploitable via stripAbsolutePath(), used by the Unpack class. An attacker can overwrite arbitrary files outside the intended extraction directory by including a hardlink whose linkpath uses a drive-relative path such as C:../target.txt in a malicious tar.

Details

A Directory Traversal attack (also known as path traversal) aims to access files and directories that are stored outside the intended folder. By manipulating files with "dot-dot-slash (../)" sequences and its variations, or by using absolute file paths, it may be possible to access arbitrary files and directories stored on file system, including application source code, configuration, and other critical system files.

Directory Traversal vulnerabilities can be generally divided into two types:

• Information Disclosure: Allows the attacker to gain information about the folder structure or read the contents of sensitive files on the system.

st is a module for serving static files on web pages, and contains a vulnerability of this type. In our example, we will serve files from the public route.

If an attacker requests the following URL from our server, it will in turn leak the sensitive private key of the root user.

curl http://localhost:8080/public/%2e%2e/%2e%2e/%2e%2e/%2e%2e/%2e%2e/root/.ssh/id_rsa
        

Note %2e is the URL encoded version of . (dot).

• Writing arbitrary files: Allows the attacker to create or replace existing files. This type of vulnerability is also known as Zip-Slip.

One way to achieve this is by using a malicious zip archive that holds path traversal filenames. When each filename in the zip archive gets concatenated to the target extraction folder, without validation, the final path ends up outside of the target folder. If an executable or a configuration file is overwritten with a file containing malicious code, the problem can turn into an arbitrary code execution issue quite easily.

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

2018-04-15 22:04:29 .....           19           19  good.txt
        2018-04-15 22:04:42 .....           20           20  ../../../../../../root/.ssh/authorized_keys
        

Remediation

Upgrade tar to version 7.5.10 or higher.

References

• GitHub Advisory
• GitHub Commit

---

Symlink Attack

---

Overview

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

Affected versions of this package are vulnerable to Symlink Attack via tar.x() extraction, which allows an attacker to overwrite arbitrary files outside the intended extraction directory with a drive-relative symlink target - like C:../../../target.txt.

PoC

        const fs = require('fs')
        const path = require('path')
        const { Header, x } = require('tar')
        
        const cwd = process.cwd()
        const target = path.resolve(cwd, '..', 'target.txt')
        const tarFile = path.join(cwd, 'poc.tar')
        
        fs.writeFileSync(target, 'ORIGINAL\n')
        
        const b = Buffer.alloc(1536)
        new Header({
          path: 'a/b/l',
          type: 'SymbolicLink',
          linkpath: 'C:../../../target.txt',
        }).encode(b, 0)
        fs.writeFileSync(tarFile, b)
        
        x({ cwd, file: tarFile }).then(() => {
          fs.writeFileSync(path.join(cwd, 'a/b/l'), 'PWNED\n')
          process.stdout.write(fs.readFileSync(target, 'utf8'))
        })
        

Details

A Directory Traversal attack (also known as path traversal) aims to access files and directories that are stored outside the intended folder. By manipulating files with "dot-dot-slash (../)" sequences and its variations, or by using absolute file paths, it may be possible to access arbitrary files and directories stored on file system, including application source code, configuration, and other critical system files.

Directory Traversal vulnerabilities can be generally divided into two types:

• Information Disclosure: Allows the attacker to gain information about the folder structure or read the contents of sensitive files on the system.

st is a module for serving static files on web pages, and contains a vulnerability of this type. In our example, we will serve files from the public route.

If an attacker requests the following URL from our server, it will in turn leak the sensitive private key of the root user.

curl http://localhost:8080/public/%2e%2e/%2e%2e/%2e%2e/%2e%2e/%2e%2e/root/.ssh/id_rsa
        

Note %2e is the URL encoded version of . (dot).

• Writing arbitrary files: Allows the attacker to create or replace existing files. This type of vulnerability is also known as Zip-Slip.

One way to achieve this is by using a malicious zip archive that holds path traversal filenames. When each filename in the zip archive gets concatenated to the target extraction folder, without validation, the final path ends up outside of the target folder. If an executable or a configuration file is overwritten with a file containing malicious code, the problem can turn into an arbitrary code execution issue quite easily.

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

2018-04-15 22:04:29 .....           19           19  good.txt
        2018-04-15 22:04:42 .....           20           20  ../../../../../../root/.ssh/authorized_keys
        

Remediation

Upgrade tar to version 7.5.11 or higher.

References

• GitHub Commit

---

Infinite loop

---

Overview

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

Affected versions of this package are vulnerable to Infinite loop via the replace method. An attacker can cause the archive scanner to enter an infinite loop by providing a tar header with a negative base-256 encoded entry size.

Remediation

Upgrade tar to version 7.5.18 or higher.

References

• GitHub Advisory
• GitHub Commit
• GitHub Release

---

Allocation of Resources Without Limits or Throttling

---

Overview

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

Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling due to the lack of enforced upper bounds on total decompressed data, entry counts, or decompression ratio in extraction and parsing paths such as src/extract.ts. An attacker can exhaust disk space and CPU resources by submitting a small crafted gzip archive with a high decompression ratio.

Remediation

Upgrade tar to version 7.5.19 or higher.

References

• GitHub Advisory
• GitHub Commit

---

Improper Verification of Cryptographic Signature

---

Overview

sigstore is a code-signing for npm packages

Affected versions of this package are vulnerable to Improper Verification of Cryptographic Signature due to the certificateOIDs option being accepted by the public API but discarded before verification, resulting in required certificate extension OIDs never being checked. An attacker can bypass intended certificate extension constraints by presenting unauthorized certificates that satisfy the remaining verification checks.

Remediation

Upgrade sigstore to version 4.1.1 or higher.

References

• GitHub Advisory
• GitHub Commit

---

Allocation of Resources Without Limits or Throttling

---

Overview

qs is a querystring parser that supports nesting and arrays, with a depth limit.

Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling via the parseArrayValue function when the comma option is in use. An attacker can exhaust system memory by submitting a parameter containing a large number of comma-separated values, resulting in the allocation of excessively large arrays.

Note: This is only exploitable if the comma option is explicitly set to true. arrayLimit is properly enforced for index and bracket notation.

PoC

const qs = require('qs');
        
        const payload = 'a=' + ','.repeat(25);  // 26 elements after split (bypasses arrayLimit: 5)
        const options = { comma: true, arrayLimit: 5, throwOnLimitExceeded: true };
        
        try {
          const result = qs.parse(payload, options);
          console.log(result.a.length);  // Outputs: 26 (bypass successful)
        } catch (e) {
          console.log('Limit enforced:', e.message);  // Not thrown
        }
        

Remediation

Upgrade qs to version 6.14.2 or higher.

References

• GitHub Commit

---

Uncaught Exception

---

Overview

qs is a querystring parser that supports nesting and arrays, with a depth limit.

Affected versions of this package are vulnerable to Uncaught Exception via stringify when arrayFormat: 'comma' is used with array elements that have a non-callable toString property. Such elements - as produced by JSON.parse, or by qs.parse under allowPrototypes or plainObjects (which yields null-prototype objects) - cause Array.prototype.join or utils.encode to throw a TypeError: Cannot convert object to primitive value, crashing the process.

Note: This is only exploitable if the arrayFormat: 'comma' option is explicitly set.

Remediation

Upgrade qs to version 6.16.1 or higher.

References

• GitHub Advisory
• GitHub Commit

---

Inefficient Algorithmic Complexity

---

Overview

Affected versions of this package are vulnerable to Inefficient Algorithmic Complexity via the uniqs helper and index-membership checks in src/parser.js when parsing flat selectors (e.g. .a.a.a...) containing a large number of class or ID tokens. The original implementation uses Array.indexOf inside a filter callback and repeated linear scans of the hasClass/hasId arrays, producing quadratic time complexity proportional to the number of tokens. An attacker who can supply selector input to the parser can trigger CPU exhaustion by providing a long flat selector, pinning a CPU core and causing the process to become unresponsive.

Remediation

Upgrade postcss-selector-parser to version 7.1.6 or higher.

References

• GitHub Advisory
• GitHub Commit
• GitHub Release

---

Regular Expression Denial of Service (ReDoS)

---

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) when processing crafted extglob patterns. An attacker can cause excessive CPU consumption and block the event loop by supplying crafted extglob patterns that trigger catastrophic backtracking in regular expressions.

Workaround

This vulnerability can be mitigated by avoiding passing untrusted glob patterns, disabling extglob support for untrusted patterns using noextglob: true, rejecting or sanitizing patterns containing nested extglobs or extglob quantifiers, enforcing strict allowlists for accepted pattern syntax, running matching in an isolated worker or separate process with time and resource limits, or applying application-level request throttling and input validation for any endpoint that accepts glob patterns.

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 picomatch to version 2.3.2, 3.0.2, 4.0.4 or higher.

References

• GitHub Advisory
• GitHub Commit

---

Regular Expression Denial of Service (ReDoS)

---

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via analyzeRepeatedExtglob in lib/parse.js, which skips single-branch repeated extglob patterns such as +(*) or +(*a). An attacker can supply these glob patterns to trigger exponential backtracking in the underlying regular expression engine against short inputs, blocking the Node.js event loop and crashing the application.

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

There is no fixed version for picomatch.

References

• GitHub PR
• Vulnerable Code

---

Integer Overflow or Wraparound

---

NVD Description

Note: Versions mentioned in the description apply only to the upstream pcre2 package and not the pcre2 package as distributed by Alpine. See How to fix? for Alpine:3.23 relevant fixed versions and status.

PCRE2 before 10.48, on 32-bit platforms, has a pcre2_pattern_convert out-of-bounds write when an attacker can provide a large pattern.

Remediation

Upgrade Alpine:3.23 pcre2 to version 10.48-r0 or higher.

References

• https://github.com/PCRE2Project/pcre2/releases/tag/pcre2-10.48
• https://github.com/PCRE2Project/pcre2/security/advisories/GHSA-q8g2-wprr-34m9

---

Free of Memory not on the Heap

---

NVD Description

Note: Versions mentioned in the description apply only to the upstream pcre2 package and not the pcre2 package as distributed by Alpine. See How to fix? for Alpine:3.23 relevant fixed versions and status.

In PCRE2 before 10.48, pcre2_jit_match mishandles a previously copied subject being passed in as a context. An incorrect free operation can occur.

Remediation

Upgrade Alpine:3.23 pcre2 to version 10.48-r0 or higher.

References

• https://github.com/PCRE2Project/pcre2/pull/937
• https://github.com/PCRE2Project/pcre2/releases/tag/pcre2-10.48

---

Regular Expression Denial of Service (ReDoS)

---

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) in route patterns. An attacker can cause resource exhaustion by supplying input with multiple sequential optional groups, leading to excessive computation and denial of service.

Workaround

This vulnerability can be mitigated by limiting the number of sequential optional groups in route patterns and avoiding user-controlled input as route patterns.

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 path-to-regexp to version 8.4.0 or higher.

References

• GitHub Commit
• OpenJS Security Advisories

---

Regular Expression Denial of Service (ReDoS)

---

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) when multiple wildcards are used in combination with at least one parameter. An attacker can cause excessive resource consumption and application unresponsiveness by supplying specially crafted input patterns that trigger catastrophic backtracking.

Note:

This is only exploitable if the second wildcard is not positioned at the end of the path.

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 path-to-regexp to version 8.4.0 or higher.

References

• GitHub Commit
• OpenJS Security Advisories

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Denial of Service (DoS)

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Overview

pacote is a JavaScript package downloader

Affected versions of this package are vulnerable to Denial of Service (DoS) via the addGitSha function. An attacker can exploit this vulnerability by supplying a specially crafted spec.rawSpec value that triggers the function’s regex replacement and string-manipulation logic, causing excessive CPU consumption and potentially stalling or crashing the process.

PoC

const addGitSha = require('pacote/lib/util/add-git-sha');
        
        var str = "";
        for (var i = 0; i < 1000000; i++) {
          str += "#";
        }
        str += "\n@";
        
        const spec = {
          rawSpec: str,
          hosted: null,
        };
        
        console.log("start");
        addGitSha(spec, "");
        console.log("end");
        
        // run `npm install pacote` and `node attack.js` 
        // then the program will stuck forever with high CPU usage
        

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 pacote to version 21.5.1 or higher.

References

• GitHub Commit
• Vulnerable Code

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Regular Expression Denial of Service (ReDoS)

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Overview

minimatch is a minimal matching utility.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). An attacker can cause excessive resource consumption and application unresponsiveness by supplying specially crafted nested extglob patterns that trigger catastrophic backtracking in the regular expression engine.

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 minimatch to version 8.0.6, 9.0.7, 10.2.3 or higher.

References

• GitHub Commit

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Inefficient Algorithmic Complexity

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Overview

minimatch is a minimal matching utility.

Affected versions of this package are vulnerable to Inefficient Algorithmic Complexity via the matchOne function. An attacker can cause significant delays in processing and stall the event loop by supplying specially crafted glob patterns containing multiple non-adjacent GLOBSTAR segments.

Remediation

Upgrade minimatch to version 3.1.3, 4.2.5, 5.1.8, 6.2.2, 7.4.8, 8.0.6, 9.0.7, 10.2.3 or higher.

References

• GitHub Commit

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