CVE-2026-58659
Lightningai Pytorch Lightning ≤ 2.6.5
Raw vector
CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2026-58659 is a high-severity Unsafe Reflection (CWE-470) vulnerability in Lightningai Pytorch Lightning. Its CVSS base score is 8.4 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Reflective Code Loading (T1620); ranked at the 25th 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 Deep Learning Frameworks; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to SI-10 (Information Input Validation) and AC-3 (Access Enforcement) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-44751
Vulnerability Data
PyTorch Lightning through 2.6.5, fixed in commit d710d68, contains a remote code execution vulnerability in the _load_state function that imports and executes attacker-controlled module names from checkpoint _instantiator hyperparameters. Attackers can craft malicious checkpoint files that bypass weights_only=True protections to…
more
execute arbitrary code when LightningModule.load_from_checkpoint is called.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Deep Learning Frameworks
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: pytorch
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Input validation directly stops externally supplied class or method names from selecting improper code via reflection.
Enforces authorization checks on the code or classes ultimately invoked, blocking unauthorized selections even if reflection is used.
Limits privileges of any code reached through unsafe reflection, reducing blast radius without stopping the selection itself.
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 avoid introducing externally controlled class selection via reflection.
Vulnerability identification processes can discover unsafe reflection during code review or scanning.
Preventing execution of unauthorized code can block exploitation of unsafe reflection at runtime.
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.
Secure coding standards directly forbid unsafe reflection and require whitelisting or static alternatives.
Security testing can detect and block unsafe reflection patterns before release.
Secure development lifecycle mandates input validation and design reviews that reduce unsafe reflection risks.
Application security requirements can explicitly prohibit or constrain reflection based on untrusted input.
Secure architecture principles discourage dynamic class loading from external data sources.
Access restrictions limit who can supply the malicious input but do not address the reflection flaw itself.