CVE-2024-2196
CSRF in Aimstack Aim 3.17.5
Raw vector
CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HSummary
CVE-2024-2196 is a high-severity CSRF (CWE-352) vulnerability in Aimstack Aim. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 45th 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 Other AI 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-2024-1169
Vulnerability Data
aimhubio/aim is vulnerable to Cross-Site Request Forgery (CSRF), allowing attackers to perform actions such as deleting runs, updating data, and stealing data like log records and notes without the user's consent. The vulnerability stems from the lack of CSRF and…
more
CORS protection in the aim dashboard. An attacker can exploit this by tricking a user into executing a malicious script that sends unauthorized requests to the aim server, leading to potential data loss and unauthorized data manipulation.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other AI Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- aimhubio/aim is an open-source ML experiment tracking, logging, and visualization platform used for managing AI/ML training runs, metrics, and data, fitting 'Other Platforms' as it is neither a framework, library, nor specialized in NLP/CV/etc.
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.