Raw vector
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-2195 is a critical-severity Code Injection (CWE-94) vulnerability in Aimstack Aim. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Junk Data (T1001.001); ranked in the top 22% 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 Machine Learning Libraries; in the Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) 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.
A critical remote code execution vulnerability affects the aimhubio/aim project in versions 3.0.0 and later. The flaw exists in the run_search_api function within aim/web/api/runs/views.py, specifically at the /api/runs/search/run/ endpoint. Improper access controls on the RunView object permit an attacker to supply arbitrary code through the query parameter, which is then executed on the server. The issue is tracked as CWE-94 and carries a CVSS 3.0 score of 9.8.
Unauthenticated remote attackers can exploit the vulnerability over the network with low attack complexity. Successful exploitation grants the ability to run arbitrary commands, resulting in full system compromise and potential loss of confidentiality, integrity, and availability.
The provided references point to a huntr.dev bounty report but contain no explicit mitigation guidance or patch details. The associated EPSS score has remained flat at 0.0838 with no observed increase since disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-1259
Vulnerability Data
A critical Remote Code Execution (RCE) vulnerability was identified in the aimhubio/aim project, specifically within the `/api/runs/search/run/` endpoint, affecting versions >= 3.0.0. The vulnerability resides in the `run_search_api` function of the `aim/web/api/runs/views.py` file, where improper restriction of user access to…
more
the `RunView` object allows for the execution of arbitrary code via the `query` parameter. This issue enables attackers to execute arbitrary commands on the server, potentially leading to full system compromise.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Machine Learning Libraries
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- The aimhubio/aim project is an open-source platform for tracking, visualizing, and comparing machine learning experiments, qualifying as an 'Other Platforms' category in AI/ML tooling.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
Input validation directly stops untrusted data from being used to construct executable code without neutralization.
Least privilege limits the damage an injected code fragment can perform once executed.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
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.
PR.PS-06's SDLC practices directly target injection flaws via secure coding and testing (mostly), yet as a single broad outcome it leaves many code-generation specifics unaddressed (partial).
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
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.