CVE-2026-47155

vLLM's revision pinning controls do not consistently apply to all artifacts loaded for a model. A deployment that supplies --revision or --code-revision can still load dynamic code, GGUF files, image processors, retrieval side weights, or same-repository subfolder weights/config from an unpinned/default revision.

This is a supply-chain integrity issue for pinned vLLM deployments. Operators can believe they are serving a reviewed model revision while vLLM resolves behavior-affecting nested or sibling artifacts outside that reviewed revision.

Details

The expected invariant is:

When a vLLM operator supplies a model or code revision pin, every code, config, processor, weight file, side weight, and same-repository subfolder artifact loaded as part of that model should resolve under that pin unless vLLM exposes and enforces a separate explicit pin for that artifact.

Current main was verified affected at commit 3795d7acf431980e62e738493f437ae2a51549da.

Affected source boundaries:

• vllm/model_executor/models/registry.py:1045-1051 and :1058-1064
  • _try_resolve_transformers() passes revision=model_config.revision and trust_remote_code=model_config.trust_remote_code, but omits code_revision=model_config.code_revision for external auto_map dynamic module imports.
• vllm/model_executor/model_loader/gguf_loader.py:58-60
  • The direct-file GGUF form repo/file.gguf calls hf_hub_download(repo_id=repo_id, filename=filename) without passing revision.
• vllm/model_executor/models/roberta.py:203-209
  • BGE-M3 secondary sparse and ColBERT side weights are declared with revision=None.
• vllm/model_executor/models/kimi_k25.py:111-114
  • Kimi-K2.5 calls cached_get_image_processor() without passing model_config.revision.
• vllm/model_executor/models/kimi_audio.py:92-95
  • Kimi-Audio loads Whisper config from the whisper-large-v3 subfolder without a revision argument.
• vllm/model_executor/models/kimi_audio.py:425-430
  • Kimi-Audio declares same-repository whisper-large-v3 secondary weights with revision=None.
• vllm/model_executor/model_loader/default_loader.py:287-301
  • The default loader preserves model_config.revision for the primary source, then consumes model-supplied secondary sources as declared.

The strongest example is Kimi-Audio: the primary moonshotai/Kimi-Audio-7B-Instruct weights preserve the configured model revision, but the same-repository whisper-large-v3 audio tower config/weights do not. A pinned Kimi-Audio deployment can therefore load the Whisper subfolder outside the audited revision.

This report does not claim a trust_remote_code=False bypass, unauthenticated RCE, or real artifact compromise. The issue is improper propagation of explicit artifact pins across supported loader paths.

Occurrences

• vllm/model_executor/models/kimi_k25.py L111-L114, Kimi-K2.5 loads its image processor with cached_get_image_processor() but does not pass self.ctx.model_config.revision. The processor can therefore resolve from the default repository revision even when the model deployment is pinned.
• vllm/model_executor/models/kimi_audio.py L425-L430, Kimi-Audio declares same-repository whisper-large-v3 secondary weights with revision=None. A pinned Kimi-Audio deployment can therefore load the Whisper audio tower weights from an unpinned/default revision.
• vllm/model_executor/models/kimi_audio.py L92-L95, Kimi-Audio loads Whisper config from the same repository's whisper-large-v3 subfolder without passing the top-level model revision. The config for this behavior-affecting subcomponent can be resolved outside the audited model revision.
• vllm/model_executor/models/registry.py L1058-L1064, The later dynamic model-class resolution repeats the same pin-decay pattern: it forwards revision and trust_remote_code, but omits code_revision. This means an operator-provided code pin is not enforced at the dynamic module loader boundary.
• vllm/model_executor/model_loader/gguf_loader.py L58-L60, The direct GGUF form repo/file.gguf calls hf_hub_download(repo_id=repo_id, filename=filename) without passing model_config.revision. A deployment that pins the model revision can therefore resolve this GGUF file from the repository default revision.
• vllm/model_executor/models/registry.py L1045-L1051, try_get_class_from_dynamic_module() is called for external auto_map config/model classes with revision=model_config.revision, but without forwarding model_config.code_revision. When --code-revision is set, this dynamic module resolution can still fall back to the default code revision instead of the audited code revision.
• vllm/model_executor/models/roberta.py L203-L209, BgeM3EmbeddingModel creates same-repository secondary sparse/ColBERT weight sources with revision=None. The primary model revision is not propagated to these side weights, so they can be downloaded outside the operator-selected model revision.

Fixes

This was fixed in: https://github.com/vllm-project/vllm/pull/42616

Originally filed via huntr: https://huntr.com/bounties/3f1e24c0-87d2-4f6c-a705-820f380879ac.

The vLLM maintainer (Russell Bryant) redirected the report to the private GHSA channel. Offline proof bundle (vllm_artifact_pin_decay_bundle_verify.py + bundle-verification-20260430T143506Z.json) is available upon request.

Affected users are operators who pin vLLM model deployments to a reviewed Hugging Face revision for safety review, provenance, rollback, or reproducibility. The impact is that the pin does not reliably describe the full set of artifacts vLLM serves. Even when the operator selects an audited revision, vLLM can resolve behavior-affecting secondary artifacts from the repository default branch or another mutable ref.

Depending on the model path, the unpinned artifact can be dynamic model code, a GGUF file, an image processor, retrieval side weights, or the same-repository Kimi-Audio Whisper subfolder weights/config.

This breaks the operational guarantee of a pinned deployment: "serve the exact artifact set I reviewed." A later change to an unpinned secondary artifact can alter model behavior without changing the operator's configured revision, making review, rollback, incident response, and audit records unreliable.

CVE-2026-47155 has a CVSS score of 6.5 (Medium). The vector is network-reachable, no privileges required, and no user interaction. A CVSS score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether this affects your application depends on whether the vulnerable code is present and reachable in your environment. A fixed version is available (0.22.0); upgrading removes the vulnerable code path.

Kodem intelligence

1. What is CVE-2026-47155? CVE-2026-47155 is a medium-severity security vulnerability in vllm (pip), affecting versions < 0.22.0. It is fixed in 0.22.0.
2. How severe is CVE-2026-47155? CVE-2026-47155 has a CVSS score of 6.5 (Medium). This score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether it represents real risk in your environment depends on whether the vulnerable code is present and reachable.
3. Which versions of vllm are affected by CVE-2026-47155? vllm (pip) versions < 0.22.0 is affected.
4. Is there a fix for CVE-2026-47155? Yes. CVE-2026-47155 is fixed in 0.22.0. Upgrade to this version or later.
5. Is CVE-2026-47155 exploitable, and should I be worried? Whether CVE-2026-47155 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
6. What actually determines whether CVE-2026-47155 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
7. How do I fix CVE-2026-47155? Upgrade vllm to 0.22.0 or later.