Raw vector
CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-14021 is a high-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Llamaindex Llamaindex. Its CVSS base score is 8.4 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Junk Data (T1001.001); ranked at the 25th 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 Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) 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-2024-14021 is an unsafe deserialization vulnerability (CWE-502) in LlamaIndex (run-llama/llama_index) versions up to and including 0.11.6. The flaw exists in the BGEM3Index.load_from_disk() function within llama_index/indices/managed/bge_m3/base.py, which invokes pickle.load() to deserialize the multi_embed_store.pkl file from a user-supplied persist_dir without validation. This allows deserialization of untrusted data loaded directly from disk.
An attacker can exploit the vulnerability by supplying a crafted persist directory containing a malicious pickle file. A victim who subsequently calls load_from_disk() on this directory will trigger arbitrary code execution. Per the CVSS v3.1 base score of 7.8 (AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H), exploitation requires local access, low complexity, no privileges, and user interaction to load the index, but grants high confidentiality, integrity, and availability impact.
Advisories and references, including those from VulnCheck (https://www.vulncheck.com/advisories/llamaindex-bgem3index-unsafe-deserialization), Huntr (https://huntr.com/bounties/ab4ceeb4-aa85-4d1c-aaca-4eda1b71fc12), the LlamaIndex GitHub repository (https://github.com/run-llama/llama_index), and the project site (https://www.llamaindex.ai/), provide further details on the issue and associated mitigations or patches. Security practitioners should consult these sources for remediation guidance.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-2396
Vulnerability Data
LlamaIndex (run-llama/llama_index) versions up to and including 0.11.6 contain an unsafe deserialization vulnerability in BGEM3Index.load_from_disk() in llama_index/indices/managed/bge_m3/base.py. The function uses pickle.load() to deserialize multi_embed_store.pkl from a user-supplied persist_dir without validation. An attacker who can provide a crafted persist directory containing…
more
a malicious pickle file can trigger arbitrary code execution when the victim loads the index from disk.
- 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: llamaindex
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
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.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
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.