Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:N/A:NSummary
CVE-2026-24123 is a high-severity Path Traversal (CWE-22) vulnerability in Bentoml Bentoml. Its CVSS base score is 7.4 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 36th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Machine Learning Libraries; 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.
CVE-2026-24123 is a path traversal vulnerability (CWE-22) affecting BentoML, a Python library for building online serving systems optimized for AI applications and model inference. In versions prior to 1.4.34, the `bentofile.yaml` configuration file permits path traversal attacks via multiple file path fields, including `description`, `docker.setup_script`, `docker.dockerfile_template`, and `conda.environment_yml`. This flaw allows malicious inputs to read arbitrary files during the bento build process.
A remote attacker with no privileges can exploit this by crafting a malicious `bentofile.yaml` and tricking a victim into building a bento with it, typically requiring user interaction such as executing the build command. Successful exploitation exfiltrates arbitrary files from the victim's filesystem—such as SSH keys, credentials, or environment variables—directly into the bento archive. These sensitive files are then silently exposed when the bento is pushed to container registries or deployed, enabling supply chain attacks. The vulnerability carries a CVSS v3.1 score of 7.4 (AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:N/A:N), reflecting high confidentiality impact with scope change.
BentoML version 1.4.34 addresses the issue with a targeted patch. Mitigation requires upgrading to this version or later. Official details are provided in the GitHub security advisory (GHSA-6r62-w2q3-48hf), release notes for v1.4.34, and the fixing commit (84d08cfeb40c5f2ce71b3d3444bbaa0fb16b5ca4).
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-4723
Vulnerability Data
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to version 1.4.34, BentoML's `bentofile.yaml` configuration allows path traversal attacks through multiple file path fields (`description`, `docker.setup_script`, `docker.dockerfile_template`, `conda.environment_yml`). An attacker can…
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craft a malicious bentofile that, when built by a victim, exfiltrates arbitrary files from the filesystem into the bento archive. This enables supply chain attacks where sensitive files (SSH keys, credentials, environment variables) are silently embedded in bentos and exposed when pushed to registries or deployed. Version 1.4.34 contains a patch for the issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Machine Learning Libraries
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai, bentoml
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.