Veille CVE
CVE-2026-93989
4.3 MoyenneDescription
vLLM through 0.29.0 fails to properly validate bad_words token indices against the model's generation output width in SamplingParams.update_from_tokenizer(). Attackers can supply out-of-bounds token indices that corrupt logits memory of concurrent requests, causing different in-flight HTTP requests to return incorrect tokens.
Produits concernés
Scores CVSS
| Version | Score | Source | Vecteur |
|---|---|---|---|
| 4.0 | 2.3 Faible | disclosure@vulncheck.com | CVSS:4.0/AV:N/AC:H/AT:P/PR:L/UI:N/VC:N/VI:L/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X |
| 3.1 | 3.1 Faible | disclosure@vulncheck.com | CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:N |
| 3.1 | 4.3 Moyenne | nvd@nist.gov (NVD) | CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:N |
Faiblesses (CWE)
Références
- https://github.com/vllm-project/vllm Product
- https://github.com/vllm-project/vllm/blob/98dff2a81d747d1dba01a47f939f48c3526d4206/vllm/sampling_params.py#L694-L753 Product
- https://github.com/vllm-project/vllm/blob/98dff2a81d747d1dba01a47f939f48c3526d4206/vllm/v1/worker/gpu/sample/bad_words.py Product
- https://github.com/vllm-project/vllm/pull/48824 Issue TrackingPatch
- https://www.vulncheck.com/advisories/vllm-through-0.29.0-cross-request-logits-corruption-via-bad-words Third Party Advisory