Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:N/A:NSummary
CVE-2026-27795 is a medium-severity SSRF (CWE-918) vulnerability in Langchain Langchain Community. Its CVSS base score is 4.1 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 11th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as NLP and Transformers; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to AC-4 (Information Flow Enforcement) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-27795 is a redirect-based Server-Side Request Forgery (SSRF) bypass in the `RecursiveUrlLoader` component of the `@langchain/community` package within the LangChainJS framework, which supports building LLM-powered applications. The vulnerability affects versions prior to 1.1.18. It occurs because the loader validates only the initial URL while allowing the underlying fetch to follow redirects automatically, enabling a shift from a safe public URL to an internal or metadata endpoint without revalidation. This serves as a bypass of SSRF protections added in version 1.1.14 for CVE-2026-26019.
Exploitation is possible over the network with low complexity by an attacker with low privileges who tricks a user into providing a specially crafted URL (AV:N/AC:L/PR:L/UI:R). The attacker can supply an initial safe public URL that redirects to restricted internal resources; when the loader processes it with user interaction, the automatic redirects bypass validation, resulting in SSRF with changed scope and low confidentiality impact (S:C/C:L). No integrity or availability impacts are possible, reflected in the CVSS v3.1 base score of 4.1 (CWE-918).
Mitigation requires upgrading to `@langchain/community` version 1.1.18, as detailed in the LangChainJS GitHub releases, commits, and pull request #9990. This patch disables automatic redirects (redirect: "manual"), resolves each 3xx Location header and validates it via `validateSafeUrl()` before following, and enforces a maximum redirect limit to avoid loops.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-8697
Vulnerability Data
LangChain is a framework for building LLM-powered applications. Prior to version 1.1.8, a redirect-based Server-Side Request Forgery (SSRF) bypass exists in `RecursiveUrlLoader` in `@langchain/community`. The loader validates the initial URL but allows the underlying fetch to follow redirects automatically, which…
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permits a transition from a safe public URL to an internal or metadata endpoint without revalidation. This is a bypass of the SSRF protections introduced in 1.1.14 (CVE-2026-26019). Users should upgrade to `@langchain/community` 1.1.18, which validates every redirect hop by disabling automatic redirects and re-validating `Location` targets before following them. In this version, automatic redirects are disabled (`redirect: "manual"`), each 3xx `Location` is resolved and validated with `validateSafeUrl()` before the next request, and a maximum redirect limit prevents infinite loops.
- CWE(s)
AI Security AnalysisAI
- AI Category
- NLP and Transformers
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: langchain, llm
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.6V1.5.3V5.3.2V10.4.7
Mitigating Controls (NIST 800-53 r5) AI
Information flow enforcement can restrict which destinations the server is allowed to contact on behalf of users.
Input validation directly stops untrusted URLs from being accepted and fetched without destination checks.
Boundary protection limits the network reach of server-initiated requests even if SSRF occurs.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure development practices directly include input validation and destination allow-listing that prevent SSRF.
Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.
Vulnerability identification processes can discover and record SSRF flaws in web applications.
Network segmentation and egress controls can limit the damage from successful SSRF requests.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.