AI Threat Alert
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.8.0 and prior to 0.8.5 are affected by a critical performance vulnerability in the input...
Full analysis pending. Showing NVD description excerpt.
Affected Systems
| Package | Ecosystem | Vulnerable Range | Patched |
|---|---|---|---|
| vllm | pip | >= 0.8.0, < 0.8.5 | 0.8.5 |
| vllm | pip | — | No patch |
Severity & Risk
Recommended Action
Patch available
Update vllm to version 0.8.5
Compliance Impact
Compliance analysis pending. Sign in for full compliance mapping when available.
Technical Details
NVD Description
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.8.0 and prior to 0.8.5 are affected by a critical performance vulnerability in the input preprocessing logic of the multimodal tokenizer. The code dynamically replaces placeholder tokens (e.g., <|audio_|>, <|image_|>) with repeated tokens based on precomputed lengths. Due to inefficient list concatenation operations, the algorithm exhibits quadratic time complexity (O(n²)), allowing malicious actors to trigger resource exhaustion via specially crafted inputs. This issue has been patched in version 0.8.5.
Weaknesses (CWE)
CVSS Vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H References
- github.com/vllm-project/vllm/security/advisories/GHSA-vc6m-hm49-g9qg Exploit Vendor
- github.com/advisories/GHSA-vc6m-hm49-g9qg
- github.com/vllm-project/vllm/blob/8cac35ba435906fb7eb07e44fe1a8c26e8744f4e/vllm/model_executor/models/phi4mm.py
- github.com/vllm-project/vllm/security/advisories/GHSA-vc6m-hm49-g9qg
- nvd.nist.gov/vuln/detail/CVE-2025-46560
- github.com/vllm-project/vllm/blob/8cac35ba435906fb7eb07e44fe1a8c26e8744f4e/vllm/model_executor/models/phi4mm.py Product
- github.com/vllm-project/vllm/security/advisories/GHSA-vc6m-hm49-g9qg Exploit Vendor