CVE-2026-54233 vLLM: OOM Denial of Service via Audio Decompression Bomb

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.23.1rc0, vLLM's /v1/audio/transcriptions endpoint limits compressed upload size but not decoded PCM output. A 25MB OPUS file expands to 14.9GB of float32 PCM at decode time. This vulnerability is fixed in 0.23.1rc0...

CVE-2026-54233 vLLM: OOM Denial of Service via Audio Decompression Bomb

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.23.1rc0, vLLM's /v1/audio/transcriptions endpoint limits compressed upload size but not decoded PCM output. A 25MB OPUS file expands to 14.9GB of float32 PCM at decode time. This vulnerability is fixed in 0.23.1rc0...

CVE-2026-54236 vLLM: incomplete CVE-2026-22778 fix leaks PIL repr addresses via Anthropic router

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.23.1rc0, the fix for CVE-2026-22778, which introduced a sanitizemessage helper that strips object-repr memory addresses from error messages before they reach the client, is incomplete: several response paths echo...

CVE-2026-54235

Summary: CVE-2026-54235 affects vLLM prior to 0.23.1rc0, where temperature validation gates using can silently mis-handle NaN and positive Infinity due to Python IEEE 754 behavior. This allows non-finite temperatures to bypass guards and propagate to GPU sampling kernels, causing undefined behav...

CVE-2026-54235 vLLM: temperature=NaN and temperature=Infinity bypass validation and propagate to GPU kernels

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.23.1rc0, ll temperature validation gates use comparison operators , which silently evaluate to False for NaN and for positive Infinity in Python's IEEE 754 float semantics. Both values pass every guard and propagat...

CVE-2026-48746 vLLM: OpenAI auth bypass

vLLM is an inference and serving engine for large language models LLMs. From 0.3.0 until 0.22.0, a vulnerability in ASGI web servers and starlette's trust on those web servers enables an authentication bypass of the OpenAI API AuthenticationMiddleware. It allows to use the API without providing t...

CVE-2026-48746 vLLM: OpenAI auth bypass

vLLM is an inference and serving engine for large language models LLMs. From 0.3.0 until 0.22.0, a vulnerability in ASGI web servers and starlette's trust on those web servers enables an authentication bypass of the OpenAI API AuthenticationMiddleware. It allows to use the API without providing t...

CVE-2026-48746

vLLM is an inference and serving engine for large language models LLMs. From 0.3.0 until 0.22.0, a vulnerability in ASGI web servers and starlette's trust on those web servers enables an authentication bypass of the OpenAI API AuthenticationMiddleware. It allows to use the API without providing t...

CVE-2026-48746

vLLM OpenAI auth bypass (CVE-2026-48746) affects vLLM versions 0.3.0 through 0.21.0. Root cause: ASGI servers and Starlette trust the Host header from the request scope, enabling manipulation of the reconstructed URL path and bypassing the OpenAI API AuthenticationMiddleware for routes beginning ...

CVE-2026-53923

vLLM is an inference and serving engine for large language models LLMs. From 0.5.5 until 0.23.1rc0, integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels csrc/quantization/gguf/ggufkernel.cu causes partial tensor processing. The output tensor is allocated at full size via...

CVE-2025-71379

vLLM versions = 0.6.3 and 0.9.0 contain multiple regular expression denial of service ReDoS vulnerabilities. Several regex patterns — in vllm/lora/utils.py, the phi4mini tool parser, and the OpenAI-compatible serving chat endpoint — are susceptible to catastrophic backtracking. An attacker...

CVE-2026-56340

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...

EUVD-2026-38129

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...

CVE-2026-56340

vLLM versions >= 0.10.2 and

CVE-2026-56340 vLLM - Denial of Service via Unvalidated Multimodal Embeddings

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...

CVE-2026-56340 vLLM - Denial of Service via Unvalidated Multimodal Embeddings

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...

EUVD-2025-210290

vLLM versions = 0.6.3 and 0.9.0 contain multiple regular expression denial of service ReDoS vulnerabilities. Several regex patterns — in vllm/lora/utils.py, the phi4mini tool parser, and the OpenAI-compatible serving chat endpoint — are susceptible to catastrophic backtracking. An attacker...

CVE-2025-71379

vLLM versions = 0.6.3 and 0.9.0 contain multiple regular expression denial of service ReDoS vulnerabilities. Several regex patterns — in vllm/lora/utils.py, the phi4mini tool parser, and the OpenAI-compatible serving chat endpoint — are susceptible to catastrophic backtracking. An attacker...

CVE-2025-71379

CVE-2025-71379 affects vLLM versions 0.6.3 through 0.8.x (before 0.9.0). The vulnerability is a set of regular expression denial of service (ReDoS) flaws in multiple components: (1) regex patterns in vllm/lora/utils.py, (2) the phi4mini tool parser, and (3) the OpenAI-compatible serving chat endp...

CVE-2025-71379 vllm - Regular Expression Denial of Service in Multiple Components

vLLM versions = 0.6.3 and 0.9.0 contain multiple regular expression denial of service ReDoS vulnerabilities. Several regex patterns — in vllm/lora/utils.py, the phi4mini tool parser, and the OpenAI-compatible serving chat endpoint — are susceptible to catastrophic backtracking. An attacker...