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With package: vllm

Found 14 matching suggestions

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Untriaged
Permalink CVE-2026-54233
6.5 MEDIUM
  • CVSS version (CVSS): 3.1
  • Attack Vector (AV): Network (N)
  • Attack Complexity (AC): Low (L)
  • Privileges Required (PR): Low (L)
  • User Interaction (UI): None (N)
  • Scope (S): Unchanged (U)
  • Confidentiality (C): None (N)
  • Integrity (I): None (N)
  • Availability (A): High (H)
  • Modified Attack Vector (MAV): Network (N)
  • Modified Attack Complexity (MAC): Low (L)
  • Modified Privileges Required (MPR): Low (L)
  • Modified User Interaction (MUI): None (N)
  • Modified Confidentiality (MC): None (N)
  • Modified Scope (MS): Unchanged (U)
  • Modified Integrity (MI): None (N)
  • Modified Availability (MA): High (H)
created 3 weeks, 2 days ago Activity log
  • Created suggestion
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.

Affected products

vllm
  • ==< 0.23.1rc0

Matching in nixpkgs

pkgs.vllm

High-throughput and memory-efficient inference and serving engine for LLMs

pkgs.pkgsRocm.vllm

High-throughput and memory-efficient inference and serving engine for LLMs

Package maintainers

Untriaged
Permalink CVE-2026-53923
5.3 MEDIUM
  • CVSS version (CVSS): 4.0
  • Attack Vector (AV): Network (N)
  • Attack Complexity (AC): Low (L)
  • Attack Requirement (AT): None (N)
  • Privileges Required (PR): None (N)
  • User Interaction (UI): Passive (P)
  • Vulnerable System Impact Confidentiality (VC): Low (L)
  • Vulnerable System Impact Integrity (VI): Low (L)
  • Vulnerable System Impact Availability (VA): None (N)
  • Subsequent System Impact Confidentiality (SC): None (N)
  • Subsequent System Impact Integrity (SI): None (N)
  • Subsequent System Impact Availability (SA): None (N)
  • Modified Attack Vector (MAV): Network (N)
  • Modified Attack Complexity (MAC): Low (L)
  • Modified Attack Requirement (MAT): None (N)
  • Modified Privileges Required (MPR): None (N)
  • Modified User Interaction (MUI): Passive (P)
  • Modified Vulnerable System Impact Confidentiality (MVC): Low (L)
  • Modified Vulnerable System Impact Integrity (MVI): Low (L)
  • Modified Vulnerable System Impact Availability (MVA): None (N)
  • Modified Subsequent System Impact Confidentiality (MSC): Negligible (N)
  • Modified Subsequent System Impact Integrity (MSI): Negligible (N)
  • Modified Subsequent System Impact Availability (MSA): Negligible (N)
  • Safety (S): Not Defined (X)
  • Automatable (AU): Not Defined (X)
  • Recovery (R): Not Defined (X)
  • Value Density (V): Not Defined (X)
  • Vulnerability Response Effort (RE): Not Defined (X)
  • Provider Urgency (U): Not Defined (X)
  • Confidentiality Req. (CR): Not Defined (X)
  • Integrity Req. (IR): Not Defined (X)
  • Availability Req. (AR): Not Defined (X)
  • Exploit Maturity (E): Not Defined (X)
created 3 weeks, 2 days ago Activity log
  • Created suggestion
vLLM GGUF Kernels: int64_t to int truncation of tensor dimensions causes GPU buffer overflow

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/gguf_kernel.cu) causes partial tensor processing. The output tensor is allocated at full size via torch::empty (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor data from other users' inference requests, constituting information disclosure. This vulnerability is fixed in 0.23.1rc0.

Affected products

vllm
  • ==>= 0.5.5, < 0.23.1rc0

Matching in nixpkgs

pkgs.vllm

High-throughput and memory-efficient inference and serving engine for LLMs

pkgs.pkgsRocm.vllm

High-throughput and memory-efficient inference and serving engine for LLMs

Package maintainers

Untriaged
Permalink CVE-2026-48746
9.1 CRITICAL
  • CVSS version (CVSS): 3.1
  • Attack Vector (AV): Network (N)
  • Attack Complexity (AC): Low (L)
  • Privileges Required (PR): None (N)
  • User Interaction (UI): None (N)
  • Scope (S): Unchanged (U)
  • Confidentiality (C): High (H)
  • Integrity (I): None (N)
  • Availability (A): High (H)
  • Modified Attack Vector (MAV): Network (N)
  • Modified Attack Complexity (MAC): Low (L)
  • Modified Privileges Required (MPR): None (N)
  • Modified User Interaction (MUI): None (N)
  • Modified Confidentiality (MC): High (H)
  • Modified Scope (MS): Unchanged (U)
  • Modified Integrity (MI): None (N)
  • Modified Availability (MA): High (H)
created 3 weeks, 2 days ago Activity log
  • Created suggestion
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 the configured VLLM_API_KEY or --api-key. This vulnerability is fixed in 0.22.0.

Affected products

vllm
  • ==>= 0.3.0, < 0.22.0

Matching in nixpkgs

pkgs.vllm

High-throughput and memory-efficient inference and serving engine for LLMs

pkgs.pkgsRocm.vllm

High-throughput and memory-efficient inference and serving engine for LLMs

Package maintainers

Untriaged
Permalink CVE-2026-47155
6.5 MEDIUM
  • CVSS version (CVSS): 3.1
  • Attack Vector (AV): Network (N)
  • Attack Complexity (AC): High (H)
  • Privileges Required (PR): None (N)
  • User Interaction (UI): None (N)
  • Scope (S): Unchanged (U)
  • Confidentiality (C): Low (L)
  • Integrity (I): High (H)
  • Availability (A): None (N)
  • Modified Attack Vector (MAV): Network (N)
  • Modified Attack Complexity (MAC): High (H)
  • Modified Privileges Required (MPR): None (N)
  • Modified User Interaction (MUI): None (N)
  • Modified Confidentiality (MC): Low (L)
  • Modified Scope (MS): Unchanged (U)
  • Modified Integrity (MI): High (H)
  • Modified Availability (MA): None (N)
created 3 weeks, 2 days ago Activity log
  • Created suggestion
vLLM: Artifact Pin Decay in vLLM allows pinned deployments to load unpinned code, weights, and processors

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, vLLM's revision pinning controls do not consistently apply to all artifacts loaded for a model. A deployment that supplies --revision or --code-revision can still load dynamic code, GGUF files, image processors, retrieval side weights, or same-repository subfolder weights/config from an unpinned/default revision. This is a supply-chain integrity issue for pinned vLLM deployments. Operators can believe they are serving a reviewed model revision while vLLM resolves behavior-affecting nested or sibling artifacts outside that reviewed revision. This vulnerability is fixed in 0.22.0.

Affected products

vllm
  • ==< 0.22.0

Matching in nixpkgs

pkgs.vllm

High-throughput and memory-efficient inference and serving engine for LLMs

pkgs.pkgsRocm.vllm

High-throughput and memory-efficient inference and serving engine for LLMs

Package maintainers