CVE-2026-27905

GHSA-m6w7-qv66-g3mf HIGH
Published March 3, 2026
CISO Take

Any BentoML deployment below 1.4.36 is vulnerable to arbitrary file write when loading external model or bento packages — patch to 1.4.36 immediately. The realistic attack path is a poisoned model distributed via public registries or third-party vendors; when your MLOps pipeline or a developer loads it, the attacker writes files anywhere on the host filesystem. Treat this as a supply chain risk: verify BentoML versions across all environments and enforce model provenance before deploying.

Affected Systems

Package Ecosystem Vulnerable Range Patched
bentoml pip < 1.4.36 1.4.36
bentoml pip No patch
bentoml pip No patch

Severity & Risk

CVSS 3.1
7.8 / 10
EPSS
0.0%
chance of exploitation in 30 days
KEV Status
Not in KEV
Sophistication
Moderate

Recommended Action

  1. 1. PATCH: Upgrade BentoML to 1.4.36 across all environments (dev, staging, prod, CI/CD) — this is the only complete fix. 2. INVENTORY: Identify all systems running BentoML using 'pip show bentoml' or equivalent; prioritize internet-facing model servers. 3. MODEL PROVENANCE: Restrict bento/model loading to internal, verified registries; block import of packages from untrusted sources until patched. 4. LEAST PRIVILEGE: Ensure BentoML processes run under dedicated service accounts with minimal filesystem permissions; avoid running as root. 5. DETECTION: Alert on unexpected file creation events outside BentoML's working directories during model loading operations (auditd or equivalent). 6. INTEGRITY: Enforce checksum or signature verification for model artifacts before extraction. 7. CONTAINER HARDENING: Review volume mounts in BentoML containers; remove unnecessary host path mounts that could be targeted.

Classification

Compliance Impact

This CVE is relevant to:

EU AI Act
Art. 15 - Accuracy, robustness and cybersecurity for high-risk AI systems Article 15 - Accuracy, robustness and cybersecurity Article 9 - Risk management system
ISO 42001
A.6.1.6 - AI supply chain security A.6.2.6 - AI system supply chain security A.8.4 - AI system security controls
NIST AI RMF
GOVERN 6.2 - Policies and procedures for AI supply chain risk management GOVERN-6.1 - Policies for third-party AI risk MANAGE 2.4 - Mechanisms for managing AI risks from third-party dependencies MEASURE-2.6 - Risk measurement and tracking
OWASP LLM Top 10
LLM05:2025 - Supply Chain Vulnerabilities

Technical Details

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 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.

Exploitation Scenario

An adversary publishes a malicious model to a public registry (e.g., HuggingFace Hub) targeting BentoML users. The bento archive contains: (1) a symlink 'models/config' pointing to '/etc/cron.d', and (2) a regular file 'models/config/pwned' containing a reverse shell cronjob. BentoML's safe_extract_tarfile() validates the symlink's own path as safe but fails to validate the symlink target. During extraction, step 1 creates the symlink pointing outside the extraction root; step 2 writes through it, depositing the reverse shell at /etc/cron.d/pwned. The cron daemon executes the payload within minutes, establishing persistent access to the model server. In CI/CD environments, this could compromise build agents and inject malicious code into downstream model artifacts before they reach production.

CVSS Vector

CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Timeline

Published
March 3, 2026
Last Modified
March 5, 2026
First Seen
March 3, 2026