CVE-2024-0765
Published: 03 March 2024
Summary
CVE-2024-0765 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Mintplexlabs Anythingllm. Its CVSS base score is 6.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Local System (T1005); ranked at the 27.7th 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 Enterprise AI Assistants; in the Privacy and Disclosure risk domain; MITRE ATLAS techniques in scope: AML.T0026.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-16553
Vulnerability details
As a default user on a multi-user instance of AnythingLLM, you could execute a call to the `/export-data` endpoint of the system and then unzip and read that export that would enable you do exfiltrate data of the system at…
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that save state. This would require the attacked to be granted explicit access to the system, but they can do this at any role. Additionally, post-download, the data is deleted so no evidence would exist that the exfiltration occured.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Enterprise AI Assistants
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- AnythingLLM is a multi-user platform for LLMs, fitting Enterprise AI Assistants. Vulnerability reported on AI/ML bug bounty platform (huntr.com), confirming AI-related nature.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Vulnerability enables low-privileged users to collect data from local system and information repositories (T1005, T1213), stage it in a ZIP archive (T1560.001, T1074.001), exfiltrate via API endpoint (T1041), with automatic file deletion post-download for indicator removal (T1070.004).
MITRE ATLAS TechniquesAI
MITRE ATLAS techniques
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.