Cyber Posture

CVE-2026-27905

HighPublic PoC

Published: 03 March 2026

Published
03 March 2026
Modified
05 March 2026
KEV Added
Patch
CVSS Score 7.8 CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
EPSS Score 0.0001 1.1th percentile
Risk Priority 16 60% EPSS · 20% KEV · 20% CVSS

Summary

CVE-2026-27905 is a high-severity Link Following (CWE-59) vulnerability in Bentoml Bentoml. Its CVSS base score is 7.8 (High).

Operationally, exploitation aligns with the MITRE ATT&CK technique Python (T1059.006); ranked at the 1.1th 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.

The strongest mitigations our analysis identified are NIST 800-53 SI-10 (Information Input Validation) and SI-2 (Flaw Remediation).

Threat & Defense at a Glance

What attackers do: exploitation maps to Python (T1059.006) and 1 other technique. What defenders deploy: see the NIST 800-53 controls recommended below.
Threat & Defense Details

Mitigating Controls (NIST 800-53 r5)AI

prevent

Directly mitigates the CVE by requiring identification, reporting, and patching of the BentoML tar extraction flaw fixed in version 1.4.36.

prevent

Mandates validation of tar file inputs to check symlink targets and prevent traversal outside the extraction directory.

prevent

Limits damage from arbitrary file writes by enforcing least privilege on the BentoML serving process accessing the host filesystem.

MITRE ATT&CK Enterprise TechniquesAI

T1059.006 Python Execution
Adversaries may abuse Python commands and scripts for execution.
T1204.002 Malicious File Execution
An adversary may rely upon a user opening a malicious file in order to gain execution.
Why these techniques?

Arbitrary file write via malicious tar (symlink traversal) directly enables writing Python payloads for execution (T1059.006) and requires user interaction with a crafted model/bento file (T1204.002).

Confidence: MEDIUM · MITRE ATT&CK Enterprise v18.1

NVD Description

BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to 1.4.36, the safe_extract_tarfile() function validates that each tar member's path is within the destination directory, but for symlink members it only…

more

validates the symlink's own path, not the symlink's target. An attacker can create a malicious bento/model tar file containing a symlink pointing outside the extraction directory, followed by a regular file that writes through the symlink, achieving arbitrary file write on the host filesystem. This vulnerability is fixed in 1.4.36.

Deeper analysisAI

CVE-2026-27905 is a vulnerability in BentoML, a Python library for building online serving systems optimized for AI applications and model inference. In versions prior to 1.4.36, the safe_extract_tarfile() function performs path validation to ensure tar members stay within the destination directory, but it only checks the symlink's own path for symlink members, neglecting the symlink's target path. This flaw, classified as CWE-59 (Improper Link Resolution Before File Access), enables symlink traversal during tar extraction.

Exploitation requires local access (AV:L) with low complexity (AC:L), no privileges (PR:N), and user interaction (UI:R), as reflected in its CVSS v3.1 base score of 7.8 (S:U/C:H/I:H/A:H). An attacker can craft a malicious bento or model tar file containing a symlink that points outside the extraction directory, followed by a regular file whose contents are written through the symlink. This achieves arbitrary file writes on the host filesystem.

The issue was addressed in BentoML 1.4.36. Mitigation involves updating to this version or later. Details on the patch are provided in the BentoML GitHub commit (https://github.com/bentoml/BentoML/commit/4e0eb007765ac04c7924220d643f264715cc9670) and security advisory (https://github.com/bentoml/BentoML/security/advisories/GHSA-m6w7-qv66-g3mf).

Details

CWE(s)

Affected Products

bentoml
bentoml
≤ 1.4.36

AI Security AnalysisAI

AI Category
Machine Learning Libraries
Risk Domain
N/A
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
Matched keywords: ai

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References