CVE-2024-37895
Published: 17 June 2024
Summary
CVE-2024-37895 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Lobehub Lobe Chat. Its CVSS base score is 5.7 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Credential Access (T1212); ranked in the top 30.0% of CVEs by exploit likelihood; 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: AI Model Inference API Access (AML.T0040).
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-2115
Vulnerability details
Lobe Chat is an open-source LLMs/AI chat framework. In affected versions if an attacker can successfully authenticate through SSO/Access Code, they can obtain the real backend API Key by modifying the base URL to their own attack URL on the…
more
frontend and setting up a server-side request. This issue has been addressed in version 0.162.25. Users are advised to upgrade. There are no known workarounds for this vulnerability.
- 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
- Lobe Chat is explicitly described as an open-source LLMs/AI chat framework, which fits the Enterprise AI Assistants category as it provides a platform for interacting with AI/LLM models via chat interfaces.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability allows authenticated attackers to steal backend API keys (application access tokens) by manipulating the frontend base URL to route requests to an attacker-controlled server, enabling exploitation for credential access, stealing application access tokens via SSRF-like mechanism, and accessing unsecured credentials.
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.