Vllm
CVE-2025-62164
HIGH
Severity by source
AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Primary rating from GitHub Advisory.
CVSS VectorGitHub Advisory
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Lifecycle Timeline
3Blast Radius
ecosystem impact- 10 pypi packages depend on vllm (10 direct, 0 indirect)
Ecosystem-wide dependent count for version 0.10.2.
DescriptionGitHub Advisory
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.
AnalysisAI
vLLM is an inference and serving engine for large language models (LLMs). Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, low attack complexity.
Technical ContextAI
This vulnerability is classified under CWE-20. 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. Affected products include: Vllm. Version information: before 0.11.1.
RemediationAI
A vendor patch is available. Apply the latest security update as soon as possible. Apply vendor patches when available. Implement network segmentation and monitoring as interim mitigations.
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Remote code execution in vLLM 0.10.1 through 0.13.x lets an attacker who controls the model repository or path run arbit
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Denial of service in vllm 0.19.0's OpenAI-compatible serving path allows remote unauthenticated attackers to exhaust sch
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Remote code execution in vLLM versions prior to 0.22.1 allows attackers to backdoor production LLM inference deployments
Same weakness CWE-20 – Improper Input Validation
View allSame technique Buffer Overflow
View allVendor StatusVendor
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External POC / Exploit Code
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