CVE-2026-47155

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, 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. This vulnerability is fixed in 0.22.0.

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, 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. This vulnerability is fixed in 0.22.0.

Summary

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.

Impact

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.

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…

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, 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. This vulnerability is fixed in 0.22.0.

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, 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. This vulnerability is fixed in 0.22.0.

Summary

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.

Impact

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.

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…

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, 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. This vulnerability is fixed in 0.22.0.