GHSA-78fp-cf4h-g36pHigh· 8.8▾ TwilightDuplicate Advisory: vLLM introduced enhanced protection for CVE-2025-62164
▾ Twilight zone — High severity, or a signal on a lesser flaw
impact 48.4 · likelihood 0 · exploitation 0
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Disclosure to exploitation, from the record and what we observed since indexing it.
Disclosed via GHSA
Last analysed / modified upstream
This advisory has been withdrawn because it is a duplicate of GHSA-mcmc-2m55-j8jj. This link is maintained to preserve external references.
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.
vllm >= 0.10.2, < 0.13.0Upgrade to a patched release:
vllm 0.13.0Connected by shared product, vendor, weakness, or advisory.
CVE-2026-56340High· 8.8vLLM versions >= 0.10.2 and < 0.13.0 are missing sparse tensor validation in multimodal embeddings processing
CVE-2026-100647Medium· 5.3vLLM versions before 0.29.0 contain a denial-of-service vulnerability in the cache_salt parameter accepted on OpenAI-compatible and Anthropic API endpoints, which lacks maximum length validation and is processed on the single EngineCore …
CVE-2026-100652Medium· 5.9vLLM versions 0.22.0 through 0.23.0 fail to validate stop_token_ids against vocabulary bounds in Rust HTTP and gRPC frontends, allowing out-of-vocabulary token IDs to reach MinTokensLogitsProcessor
CVE-2026-93989Low· 3.1vLLM through 0.29.0 fails to properly validate bad_words token indices against the model's generation output width in SamplingParams.update_from_tokenizer()
CVE-2026-93841Low· 3.7vLLM through 0.29.0 contains a memory corruption vulnerability in the Triton _bincount_kernel where prompt token IDs index the penalty prompt-presence bitset without bounds checking against vocabulary size
CVE-2026-93840Low· 3.7vLLM before 0.29.0 validates allowed_token_ids against tokenizer length instead of model output logits width in SamplingParams._validate_allowed_token_ids()