CVE-2024-5186
SSRF in Pribai Privategpt 0.5.0
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:L/I:L/A:NSummary
CVE-2024-5186 is a high-severity SSRF (CWE-918) vulnerability in Pribai Privategpt. Its CVSS base score is 7.2 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 27th 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 LLM Application Platforms; in the Privacy and Disclosure 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.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-46430
Vulnerability Data
A Server-Side Request Forgery (SSRF) vulnerability exists in the file upload section of imartinez/privategpt version 0.5.0. This vulnerability allows attackers to send crafted requests that could result in unauthorized access to the local network and potentially sensitive information. Specifically, by…
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manipulating the 'path' parameter in a file upload request, an attacker can cause the application to make arbitrary requests to internal services, including the AWS metadata endpoint. This issue could lead to the exposure of internal servers and sensitive data.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- PrivateGPT is an open-source platform for running private LLMs for document querying and RAG, fitting as an 'Other Platforms' AI application. The SSRF vulnerability is in its file upload feature, part of the deployed AI service.
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.