Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:L/I:L/A:LSummary
CVE-2024-31584 is a medium-severity Out-of-bounds Read (CWE-125) vulnerability in Linuxfoundation Pytorch. Its CVSS base score is 5.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Privilege Escalation (T1068); ranked at the 31th 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 Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SA-8 (Security and Privacy Engineering Principles) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-29459
Vulnerability Data
Pytorch before v2.2.0 has an Out-of-bounds Read vulnerability via the component torch/csrc/jit/mobile/flatbuffer_loader.cpp.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Deep Learning Frameworks
- Risk Domain
- Other ATLAS/OWASP Terms
- 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 component (flatbuffer_loader.cpp), which handles model serialization and loading.
Related Threats
MITRE ATT&CK Enterprise Techniques
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Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation directly finds out-of-bounds read flaws through static analysis, fuzzing, and dynamic bounds checks.
Secure engineering principles require bounds checking and memory-safe constructs that stop out-of-bounds reads from being introduced.
Process isolation confines the effects of an out-of-bounds read to the compromised process.
Input validation rejects malformed indices or lengths that would otherwise cause reads outside buffer bounds.
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 such as bounds checking and memory-safe languages directly prevent out-of-bounds reads.
Vulnerability scanning and recording can discover instances of out-of-bounds reads after code is deployed.
Routine patching replaces vulnerable code containing out-of-bounds read flaws.
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 and acceptance includes fuzzing and static analysis that detect out-of-bounds read defects before release.
Logging can record evidence of an out-of-bounds read but does not prevent the weakness itself.
Secure development life cycle mandates input validation and bounds checking that directly prevent out-of-bounds reads.
Application security requirements include explicit bounds and memory-safety specifications that mitigate buffer over-reads.
Secure system architecture and engineering principles require memory-safe design patterns and runtime protections against out-of-bounds access.
Secure coding standards explicitly forbid unsafe pointer arithmetic and mandate bounds-checked reads, eliminating CWE-125.