CVE-2026-5843

HighCVSS 8.2

Published: 22/05/2026(UTC)Updated: 23/07/2026(UTC)Translated: 25/07/2026(UTC)NVD NIST

Exploitation Probability (EPSS)

Low risk

0.22%

13th percentile - higher than 13% of all known CVEs

Summary

The MLX inference backend in Docker Model Runner on macOS uses the MLX-LM library, which unconditionally imports and executes arbitrary Python files from model directories via the model_file configuration field in config.json. No sandboxing allows remote code execution on the Docker host as the Docker Desktop user.

Risk Assessment

Any container on the Docker network can trigger arbitrary code execution on the host, leading to full system compromise.

Recommendation

Restrict access to the model-runner.docker.internal API and avoid using untrusted models from OCI registries.

Other vulnerabilities in Docker Model Runner

CVE-2026-5817High
The vllm-metal inference backend in Docker Model Runner on macOS unconditionally sets trust_remote_code=True when loading model tokenizers and runs without sandboxing. This causes arbitrary Python file execution from the model, enabling remote code execution on the Docker host as the Docker Desktop user.

Original NVD description (English source)

The MLX inference backend in Docker Model Runner on macOS uses the MLX-LM library, which unconditionally imports and executes arbitrary Python files from model directories via the model_file configuration field in config.json. When a model's config.json specifies a model_file pointing to a Python file, MLX-LM uses importlib to load and execute it with no trust_remote_code gate or equivalent safety check. The MLX backend runs without sandboxing, resulting in arbitrary code execution on the Docker host as the Docker Desktop user. Any container on the Docker network can trigger this by calling the model-runner.docker.internal API to pull a malicious model from an attacker-controlled OCI registry and request inference.