CVE-2026-40610
Bentoml ≤ 1.4.39
Raw vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:NSummary
CVE-2026-40610 is a medium-severity Link Following (CWE-59) vulnerability in Bentoml Bentoml. Its CVSS base score is 5.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Path Interception (T1034); ranked at the 21th 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 Machine Learning Libraries; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and AC-6 (Least Privilege) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-31497
Vulnerability Data
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. In versions 1.4.38 and prior, the build packaging workflow follows attacker-controlled symlinks inside the build context and copies the referenced file contents into…
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the generated Bento artifact. If a victim builds an untrusted repository or other attacker-supplied build context, the attacker can place a symlink such as loot.txt -> /tmp/outside-marker.txt or a link to a more sensitive local file. When bentoml build runs, BentoML dereferences the symlink and packages the target file contents into the Bento. The leaked file can then propagate further through export, push, or containerization workflows. An attacker can exfiltrate local files from the build host into the Bento artifact, exposing secrets such as cloud credentials, SSH keys, API tokens, environment files, or other sensitive local configurations. Because Bento artifacts are commonly exported, uploaded, stored, or containerized after build, the leaked file contents can spread beyond the original build machine. This issue has been fixed in version 1.4.39.
- 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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V15.4.2
Mitigating Controls (NIST 800-53 r5) AI
Proper enforcement of access authorizations on the resolved target resource stops a link from reaching an unintended object.
Least-privilege limits the damage an attacker can cause after following an unintended link.
Validating file-name inputs can reject or canonicalize names that resolve to links before access occurs.
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 require code to validate paths and avoid unsafe link following.
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 can detect link-following flaws before release.
Secure SDLC practices can mandate link-resolution checks and canonicalization before file access.
Application security requirements can explicitly require safe handling of symbolic links and path traversal.
Secure architecture principles include input validation and safe file-access design patterns.
Secure coding standards directly address canonicalization and symlink attacks during implementation.
Access-control rules can limit which files are reachable, reducing exposure to malicious links.