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VulnerabilityPublished Updated

CVE-2026-5817: Docker Desktop · Untrusted Functionality Inclusion

Affects Docker DesktopSource CVE Database V5

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Not known to be exploited · CVSS 8.8 High : upgrade to 4.68.0 soon.

Data as of 30 Oct 2026, 11:52 UTC

CVSS 4.0 base score
8.8High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.2% chance of exploitation in 30 days – higher than 13% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 4.68.0
CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 22 May 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: evidence found.

    Public exploit code: GitHub proofs of concept

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), GitHub proofs of concept, the Metasploit, Exploit-DB and nuclei-templates index as of 30 Oct 2026.

What to do

Upgrade Docker Desktop to 4.68.0.

Affected software

ProductAffectedFixed in
DockerDocker DesktopAffected
  • >=4.62.0 <4.68.0
Fixed in
  • 4.68.0

AI-suggested

If you cannot patch yet, users should consider restricting network access to the model-runner.docker.internal API and avoid running untrusted containers or pulling untrusted models that could trigger inference. Monitoring vendor communications for updates is recommended.

Description

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 transformers.AutoTokenizer.from_pretrained() to import and execute arbitrary Python files included in any model pulled from an OCI registry, resulting in arbitrary code execution on the Docker host as the Docker Desktop user when inference is triggered. Any container on the Docker network can trigger this by calling the model-runner.docker.internal API to pull a malicious model and request inference.

References

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