CVE-2024-5980
Path Traversal in Lightningai Pytorch Lightning 2.2.4 – 2.3.3
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2024-5980 is a critical-severity Path Traversal (CWE-22) vulnerability in Lightningai Pytorch Lightning. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 32% 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 Deep Learning Frameworks; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) 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 path traversal vulnerability exists in the /v1/runs API endpoint of lightning-ai/pytorch-lightning version 2.2.4. When the LightningApp runs with the plugin_server enabled, the endpoint processes tar.gz plugin uploads without properly sanitizing embedded paths during extraction. This flaw, tracked as CWE-22, permits writes to arbitrary locations on the host filesystem and carries a CVSS 3.1 score of 9.8.
An unauthenticated remote attacker can upload a crafted tar.gz archive containing files that use “..” sequences. Successful exploitation results in arbitrary file placement, which can be leveraged to achieve remote code execution on the victim system.
A fix is available in the referenced commit that addresses unsafe path handling during tar extraction. The issue was disclosed through a public huntr bounty report that includes reproduction details and the patch location. The associated EPSS score has remained at 0.1073 with no material increase since disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-2112
Vulnerability Data
A vulnerability in the /v1/runs API endpoint of lightning-ai/pytorch-lightning v2.2.4 allows attackers to exploit path traversal when extracting tar.gz files. When the LightningApp is running with the plugin_server, attackers can deploy malicious tar.gz plugins that embed arbitrary files with path…
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traversal vulnerabilities. This can result in arbitrary files being written to any directory in the victim's local file system, potentially leading to remote code execution.
- 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
- The vulnerability affects lightning-ai/pytorch-lightning v2.2.4, a popular wrapper library for PyTorch, which is a deep learning framework used for training and managing deep learning models.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V5.3.2
Mitigating Controls (NIST 800-53 r5) AI
Enforces the intended directory access authorizations that path traversal would otherwise bypass.
Input validation directly neutralizes special path elements before pathname construction occurs.
Least privilege reduces the impact of any unauthorized file access obtained via traversal.
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.
Patching/maintenance can remediate known path-traversal flaws in deployed software (partial prevention of exploitability) but does nothing to stop the coding defect from being introduced in the first place.
PR.AA-05 defines and reviews access policies but does not address code-level pathname neutralization, so neither direction prevents CWE-22.
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 catches path traversal via static/dynamic analysis.
Secure SDLC mandates input validation and path sanitization that directly prevent path traversal.
Application security requirements include rules for safe file handling and canonicalization.
Secure architecture principles require least-privilege file access and directory isolation.
Secure coding standards explicitly forbid unsafe path construction and mandate safe APIs.
Information access restriction limits which files an application may read or write.