Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:C/C:H/I:L/A:HSummary
CVE-2024-47066 is a critical-severity SSRF (CWE-918) vulnerability in Lobehub Lobe Chat. Its CVSS base score is 9.0 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 4% 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 LLM Application Platforms; in the Other ATLAS/OWASP Terms 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.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
Lobe Chat is an open-source AI chat framework that contains a server-side request forgery vulnerability in versions prior to 1.19.13. The flaw resides in the proxy handler at src/app/api/proxy/route.ts, where SSRF protections fail to account for HTTP redirects, allowing an attacker-supplied external URL to reach internal targets such as private network addresses or loopback interfaces. The issue is tracked as CWE-918 and carries a CVSS 3.1 score of 9.0.
An authenticated user with administrative privileges can supply a malicious URL that redirects to internal resources, bypassing the intended network restrictions and potentially reading or interacting with services that should remain inaccessible from outside the application. Successful exploitation can result in high confidentiality and availability impact along with limited integrity effects and a scope change.
The project’s GitHub security advisories and the commit e960a23b0c69a5762eb27d776d33dac443058faf document the improved redirect handling introduced in version 1.19.13; administrators are advised to upgrade to that release to close the bypass.
The associated EPSS score rose from a low baseline to a peak of 0.0813, indicating emerging exploitation interest after public disclosure of the SSRF issue in this AI-oriented application.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-2703
Vulnerability Data
Lobe Chat is an open-source artificial intelligence chat framework. Prior to version 1.19.13, server-side request forgery protection implemented in `src/app/api/proxy/route.ts` does not consider redirect and could be bypassed when attacker provides an external malicious URL which redirects to internal resources…
more
like a private network or loopback address. Version 1.19.13 contains an improved fix for the issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Lobe Chat is an open-source AI chat framework designed for interacting with AI models, fitting the Enterprise AI Assistants category as it provides a platform for AI-powered chat interfaces and assistants.
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.