FKIE_CVE-2025-68665
Vulnerability from fkie_nvd - Published: 2025-12-23 23:15 - Updated: 2026-06-17 09:59
Severity
8.6 (High) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:N
9.1 (Critical) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N
9.1 (Critical) - CVSS:3.1/
Summary
LangChain is a framework for building LLM-powered applications. Prior to @langchain/core versions 0.3.80 and 1.1.8, and prior to langchain versions 0.3.37 and 1.2.3, a serialization injection vulnerability exists in LangChain JS's toJSON() method (and subsequently when string-ifying objects using JSON.stringify(). The method did not escape objects with 'lc' keys when serializing free-form data in kwargs. The 'lc' key is used internally by LangChain to mark serialized objects. When user-controlled data contains this key structure, it is treated as a legitimate LangChain object during deserialization rather than plain user data. This issue has been patched in @langchain/core versions 0.3.80 and 1.1.8, and langchain versions 0.3.37 and 1.2.3
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| langchain | langchain.js | * | |
| langchain | langchain.js | * | |
| langchain | langchain\/core | * | |
| langchain | langchain\/core | * |
{
"affected": [
{
"affectedData": [
{
"product": "langchainjs",
"vendor": "langchain-ai",
"versions": [
{
"status": "affected",
"version": "@langchain/core \u003e= 1.0.0, \u003c 1.1.8"
},
{
"status": "affected",
"version": "@langchain/core \u003c 0.3.80"
},
{
"status": "affected",
"version": "langchain \u003e= 1.0.0, \u003c 1.2.3"
},
{
"status": "affected",
"version": "langchain \u003c 0.3.37"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:langchain:langchain.js:*:*:*:*:*:*:*:*",
"matchCriteriaId": "79F6F9C9-7927-4C72-9F8F-63186F6D59A2",
"versionEndExcluding": "0.3.37",
"vulnerable": true
},
{
"criteria": "cpe:2.3:a:langchain:langchain.js:*:*:*:*:*:*:*:*",
"matchCriteriaId": "32341D52-B3FD-484F-940F-CB8EB24CFB43",
"versionEndExcluding": "1.2.3",
"versionStartIncluding": "1.0.0",
"vulnerable": true
},
{
"criteria": "cpe:2.3:a:langchain:langchain\\/core:*:*:*:*:*:node.js:*:*",
"matchCriteriaId": "7D44DA1A-0433-4A46-8E85-D8363F1E5C99",
"versionEndExcluding": "0.3.80",
"vulnerable": true
},
{
"criteria": "cpe:2.3:a:langchain:langchain\\/core:*:*:*:*:*:node.js:*:*",
"matchCriteriaId": "CECC1942-DCA4-42B5-B9C1-E2F420B27D9A",
"versionEndExcluding": "1.1.8",
"versionStartIncluding": "1.0.0",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "LangChain is a framework for building LLM-powered applications. Prior to @langchain/core versions 0.3.80 and 1.1.8, and prior to langchain versions 0.3.37 and 1.2.3, a serialization injection vulnerability exists in LangChain JS\u0027s toJSON() method (and subsequently when string-ifying objects using JSON.stringify(). The method did not escape objects with \u0027lc\u0027 keys when serializing free-form data in kwargs. The \u0027lc\u0027 key is used internally by LangChain to mark serialized objects. When user-controlled data contains this key structure, it is treated as a legitimate LangChain object during deserialization rather than plain user data. This issue has been patched in @langchain/core versions 0.3.80 and 1.1.8, and langchain versions 0.3.37 and 1.2.3"
}
],
"id": "CVE-2025-68665",
"lastModified": "2026-06-17T09:59:24.050",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 8.6,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "CHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 4.0,
"source": "security-advisories@github.com",
"type": "Secondary"
},
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 9.1,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 5.2,
"source": "nvd@nist.gov",
"type": "Primary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2025-68665",
"options": [
{
"exploitation": "poc"
},
{
"automatable": "yes"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2025-12-24T14:38:36.252592Z",
"version": "2.0.3"
}
}
]
},
"published": "2025-12-23T23:15:45.097",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Patch"
],
"url": "https://github.com/langchain-ai/langchainjs/commit/e5063f9c6e9989ea067dfdff39262b9e7b6aba62"
},
{
"source": "security-advisories@github.com",
"tags": [
"Release Notes"
],
"url": "https://github.com/langchain-ai/langchainjs/releases/tag/%40langchain%2Fcore%401.1.8"
},
{
"source": "security-advisories@github.com",
"tags": [
"Release Notes"
],
"url": "https://github.com/langchain-ai/langchainjs/releases/tag/langchain%401.2.3"
},
{
"source": "security-advisories@github.com",
"tags": [
"Vendor Advisory"
],
"url": "https://github.com/langchain-ai/langchainjs/security/advisories/GHSA-r399-636x-v7f6"
},
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"tags": [
"Vendor Advisory"
],
"url": "https://github.com/langchain-ai/langchainjs/security/advisories/GHSA-r399-636x-v7f6"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-502"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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