CVE-2025-6211
Llamaindex ≤ 0.3.1
Raw vector
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:LSummary
CVE-2025-6211 is a medium-severity Expected Behavior Violation (CWE-440) vulnerability in Llamaindex Llamaindex. Its CVSS base score is 6.5 (Medium).
Operationally, ranked at the 24th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as NLP and Transformers; in the Data-Related Vulnerabilities risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-6 (Security and Privacy Function Verification) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-20993
Vulnerability Data
A vulnerability in the DocugamiReader class of the run-llama/llama_index repository, up to version 0.12.28, involves the use of MD5 hashing to generate IDs for document chunks. This approach leads to hash collisions when structurally distinct chunks contain identical text, resulting…
more
in one chunk overwriting another. This can cause loss of semantically or legally important document content, breakage of parent-child chunk hierarchies, and inaccurate or hallucinated responses in AI outputs. The issue is resolved in version 0.3.1.
- CWE(s)
AI Security AnalysisAI
- AI Category
- NLP and Transformers
- Risk Domain
- Data-Related Vulnerabilities
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
Related Threats
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation directly checks whether implemented functions match their specifications.
Security function verification confirms that functions operate according to their defined expected behavior.
Requiring a documented security architecture and design reduces the chance that implementation deviates from intended behavior.
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 SDLC practices directly enforce specification compliance and catch expected-behavior violations during development.
Security testing and exercises help discover behavior deviations before deployment.
Vulnerability identification can surface spec-violating flaws, while eliminating the weakness reduces some vulnerability backlog.
Routine software maintenance and patching can remediate discovered specification violations.
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.
Security testing in development and acceptance validates that functions behave as specified.
Secure development life cycle mandates verification against specifications, directly reducing expected-behavior violations.
Application security requirements explicitly define expected behavior that must be met.
Secure coding practices enforce adherence to functional specifications during implementation.
Change management can catch specification deviations introduced by modifications.
Documented operating procedures reduce the chance that functions deviate from intended behavior.