CVE-2024-4084
SSRF in Mintplexlabs Anythingllm ≤ 1.5.4
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2024-4084 is a high-severity SSRF (CWE-918) vulnerability in Mintplexlabs Anythingllm. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 40th 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 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.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-32646
Vulnerability Data
A Server-Side Request Forgery (SSRF) vulnerability exists in the latest version of mintplex-labs/anything-llm, allowing attackers to bypass the official fix intended to restrict access to intranet IP addresses and protocols. Despite efforts to filter out intranet IP addresses starting with…
more
192, 172, 10, and 127 through regular expressions and limit access protocols to HTTP and HTTPS, attackers can still bypass these restrictions using alternative representations of IP addresses and accessing other ports running on localhost. This vulnerability enables attackers to access any asset on the internal network, attack web services on the internal network, scan hosts on the internal network, and potentially access AWS metadata endpoints. The vulnerability is due to insufficient validation of user-supplied URLs, which can be exploited to perform SSRF attacks.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Enterprise AI Assistants
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- mintplex-labs/anything-llm is an open-source AI application for running LLMs locally or with providers, featuring chat interfaces, document management, and agents, fitting the Enterprise AI Assistants category. The SSRF vulnerability occurs in its server handling user-supplied URLs, confirmed AI-related via AI/ML bug bounty platform.
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.