Bug 2416282 (CVE-2025-62164)

Summary: CVE-2025-62164 vllm: VLLM deserialization vulnerability leading to DoS and potential RCE
Product: [Other] Security Response Reporter: OSIDB Bzimport <bzimport>
Component: vulnerabilityAssignee: Product Security DevOps Team <prodsec-dev>
Status: NEW --- QA Contact:
Severity: high Docs Contact:
Priority: high    
Version: unspecifiedCC: alinfoot, bbrownin, dtrifiro, jkoehler, lphiri, rbryant, weaton
Target Milestone: ---Keywords: Security
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Hardware: All   
OS: Linux   
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A vulnerability in vLLM allows attackers to supply malicious serialized prompt-embedding tensors that are deserialized using torch.load() without validation. Due to PyTorch 2.8.0 disabling sparse-tensor integrity checks by default, a crafted tensor can bypass bounds checks and cause an out-of-bounds write during to_dense(), leading to a crash (DoS) and potentially remote code execution on the vLLM server.
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oVirt Team: --- RHEL 7.3 requirements from Atomic Host:
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Description OSIDB Bzimport 2025-11-21 02:01:35 UTC 
vLLM is an inference and serving engine for large language models (LLMs). From versions 0.10.2 to before 0.11.1, a memory corruption vulnerability could lead to a crash (denial-of-service) and potentially remote code execution (RCE), exists in the Completions API endpoint. When processing user-supplied prompt embeddings, the endpoint loads serialized tensors using torch.load() without sufficient validation. Due to a change introduced in PyTorch 2.8.0, sparse tensor integrity checks are disabled by default. As a result, maliciously crafted tensors can bypass internal bounds checks and trigger an out-of-bounds memory write during the call to to_dense(). This memory corruption can crash vLLM and potentially lead to code execution on the server hosting vLLM. This issue has been patched in version 0.11.1.