CVE-2026-115915

Vulnerability in vLLM versions 0.3.0 to 0.22.0 allows bypass of OpenAI API AuthenticationMiddleware. An attacker can use the API without providing the configured VLLM_API_KEY or --api-key.

A vulnerability in vLLM from version 0.8.3 to 0.14.0 leaks a heap address when an invalid image is sent to the multimodal endpoint. This leak reduces ASLR effectiveness from 4 billion to about 8 guesses, facilitating further attacks.

vLLM from 0.16.0 until 0.32.0 allows an authenticated caller to place an attacker-controlled template in chat_template_kwargs[chat_template] when no server template is configured. ChatParams.get_apply_chat_template_kwargs() preserves that value without enforcing trust_request_chat_template, bypassing the trust policy and causing CPU or memory exhaustion through Jinja rendering.

vLLM is an inference and serving engine for large language models. Prior to 0.32.0, the OpenAI-compatible server configures Starlette CORSMiddleware with allowed_origins set to a wildcard by default. A malicious web page can use a victim's browser to reach an unauthenticated vLLM server on localhost, an internal network, or a VPN, call endpoints including GET /v1/models and POST /v1/chat/completions, consume GPU resources, and read responses. This issue is fixed in version 0.32.0.

vLLM before 0.32.0, in vllm/tokenizers/hf.py, computes max_token_id with an off-by-one upper bound, allowing a pre-tokenized prompt ID equal to the vocabulary size to pass validation. For tensor-parallel size one and an exact multiple-of-64 vocabulary, VocabParallelEmbedding.forward does not mask that value before F.embedding uses it, causing a CUDA device-side assertion and terminating the engine.

vLLM from 0.11.1 until 0.32.0, in the DeepSeek-OCR, DeepSeek-OCR-2, and Unlimited-OCR image path, allows a remote client reaching the OpenAI-compatible API to submit a legal image whose normalized pixels sum to zero when alone in its per-step encoder batch. _parse_and_validate_image_input returns no embedding, embed_multimodal propagates that value, and sanity_check_mm_encoder_outputs triggers a fatal assertion that shuts down the engine for all users.

vLLM is an inference and serving engine for large language models. Prior to 0.32.0, the Phi-4 multimodal audio encoder in phi4mm_audio.py and phi4mm_utils.py uses eager attention by default, allowing a remote client to submit one long audio clip that materializes full attention matrices and causes quadratic GPU-memory use, leading to out-of-memory and termination of EngineCore for all users.

Prior to 0.32.0, an authenticated caller in vLLM can place forged shared-memory address and monotonic_id values in public multimodal features.kwargs_data. In scale-out deployments with shared-memory multimodal caching enabled, workers trust those values and pass attacker-selected bytes to pickle.loads(), allowing arbitrary Python execution even when insecure serialization is disabled.

vLLM is an inference and serving engine for large language models. From 0.19.0 until 0.32.0, authenticated callers can supply a chat template when no server-side --chat-template is configured, or a malicious model repository can provide a template compiled and rendered by tokenizer.apply_chat_template() without a wall-clock timeout, output-size limit, or iteration bound. Rendering runs in a ThreadPoolExecutor with one worker, so a nonterminating render can block /v1/responses and other chat-template users, while an output-expanding render can exhaust server memory.

vLLM from 0.20.1 before 0.29.0 contains an improper input validation vulnerability that allows remote attackers to crash the engine by supplying out-of-vocabulary stop_token_ids. Attackers can send OpenAI chat, completion, generate, or batch requests with invalid IDs to terminate the engine or write outside the V2 runner's logits buffer.