CVE-2024-3135
CSRF in Mudler Localai ≤ 2.17.0
Raw vector
CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:HSummary
CVE-2024-3135 is a medium-severity CSRF (CWE-352) vulnerability in Mudler Localai. Its CVSS base score is 6.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 22th 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 Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and SC-23 (Session Authenticity) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-1234
Vulnerability Data
A Cross-Site Request Forgery (CSRF) vulnerability exists in the mudler/localai application, allowing attackers to craft malicious webpages that, when visited by a victim, perform unauthorized actions on the victim's local LocalAI instance without their consent. This vulnerability enables attackers to…
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exhaust system resources, consume credits, and fill disk space by making numerous resource-intensive API calls, such as generating images or uploading files. The vulnerability stems from the application's acceptance of simple request content-types without requiring CSRF tokens or implementing other CSRF mitigation measures. Successful exploitation does not require network access to the vulnerable LocalAI environment.
- 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
- LocalAI (mudler/localai) is a self-hosted, open-source platform for running AI models locally with an OpenAI-compatible API, supporting inference for LLMs, image generation, and more. It fits as an 'Other Platforms' category for AI serving and deployment platforms.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V3.3.2V3.5.1V10.2.1
Mitigating Controls (NIST 800-53 r5) AI
Access enforcement requires verifying that state-changing requests originate from the authenticated user rather than a forged cross-site source.
Protecting session authenticity prevents attackers from replaying or forging authenticated requests via the victim's browser.
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 SDLC practices directly require anti-CSRF controls such as tokens or SameSite attributes.
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.
By denying access to phishing or malicious sites, the control lowers the likelihood that a user will be tricked into submitting a forged request that performs an unintended action on another site.
Contextual intelligence about emerging CSRF toolkits can be translated into updated anti-CSRF token or same-site policy configurations across applications.