vLLM - Denial of Service via Unvalidated Multimodal Embeddings
Default status is the baseline for the product, each version can override it (e.g. patched versions marked unaffected).
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| vLLM | vLLM | unaffected |
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No data.
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| Vllm | Vllm |
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| Source | ID | Title |
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Github GHSA |
GHSA-mcmc-2m55-j8jj | vLLM introduced enhanced protection for CVE-2025-62164 |
No CVSS v3.0
No CVSS v2
This CVE is not in the KEV list.
Exploit prediction score: < 1% (0.644% probability within 30 days)
| Type | Values Removed | Values Added |
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| Weaknesses | CWE-787 | |
| References |
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| Metrics |
threat_severity
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threat_severity
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| Type | Values Removed | Values Added |
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| Metrics |
ssvc
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| Description | vLLM versions >= 0.10.2 and < 0.13.0 are missing sparse tensor validation in multimodal embeddings processing. Because PyTorch disables sparse tensor invariant checks by default, an attacker can submit crafted embedding requests with malformed (negative or out-of-bounds) tensor indices, when the prompt-embeds feature is enabled, to trigger crashes or resource exhaustion (denial of service), with potential for out-of-bounds/write-what-where memory corruption. This continues CVE-2025-62164, whose prior fix only disabled the feature by default rather than addressing the root cause. | |
| Title | vLLM - Denial of Service via Unvalidated Multimodal Embeddings | |
| First Time appeared |
Vllm
Vllm vllm |
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| Weaknesses | CWE-20 | |
| CPEs | cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:* | |
| Vendors & Products |
Vllm
Vllm vllm |
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| References |
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| Metrics |
cvssV3_1
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