Raw vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HSummary
CVE-2024-31583 is a high-severity Use After Free (CWE-416) vulnerability in Linuxfoundation Pytorch. Its CVSS base score is 7.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Client Execution (T1203); ranked at the 18th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Deep Learning Frameworks; in the Not Applicable risk domain; MITRE ATLAS techniques in scope: External Harms (AML.T0048).
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-1265
Vulnerability Data
Pytorch before version v2.2.0 was discovered to contain a use-after-free vulnerability in torch/csrc/jit/mobile/interpreter.cpp.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Deep Learning Frameworks
- Risk Domain
- Not Applicable
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- PyTorch is a widely used deep learning framework, and the vulnerability is in its core JIT mobile interpreter component (torch/csrc/jit/mobile/interpreter.cpp), directly affecting AI model execution.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Use-after-free vulnerability in PyTorch's JIT mobile interpreter enables arbitrary code execution via crafted TorchScript models, facilitating Exploitation for Client Execution.
MITRE ATLAS TechniquesAI
MITRE ATLAS techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 3 hardening rules · 3 OS baselines
V1.4.3
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Use-after-free exploits that achieve arbitrary code execution are blocked or significantly hardened by non-executable pages and ASLR.
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 incorporate memory-safety tooling and reviews that prevent most use-after-free defects.
Vulnerability identification processes can discover use-after-free issues via scanning or analysis but do not prevent their introduction.
Routine patching removes known use-after-free instances after they have been introduced in released software.
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.
Security testing in development can detect use-after-free bugs before release.
Secure SDLC mandates memory-safety practices that reduce use-after-free defects.
Application security requirements can specify memory-management rules that mitigate use-after-free.
Secure architecture principles include memory-safety design choices that limit use-after-free exposure.
Secure coding standards directly prescribe avoidance of use-after-free patterns.
Change-management processes help ensure memory-safety fixes are deployed consistently.