PYSEC-2026-568 vLLM deserialization vulnerability in vllm.distributed.GroupCoordinator.recv_object

vllm-project vllm version 0.6.0 contains a vulnerability in the distributed training API. The function vllm.distributed.GroupCoordinator.recvobject deserializes received object bytes using pickle.loads without sanitization, leading to a remote code execution vulnerability. Maintainer perspective...

CVE-2025-71379

A flaw was found in vLLM. Multiple regular expression denial of service ReDoS vulnerabilities exist in versions greater than or equal to 0.6.3 and less than 0.9.0. An attacker can exploit this by submitting crafted input with nested or repeated structures to specific regex patterns within vLLM,...

CVE-2026-56340

A flaw was found in vLLM. This vulnerability allows a remote attacker to trigger crashes or resource exhaustion, leading to a denial of service DoS. By submitting specially crafted embedding requests with malformed tensor indices, when the prompt-embeds feature is enabled, an attacker could also...

CVE-2026-53923

A flaw was found in vLLM. Integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels leads to partial tensor processing. This results in the output tensor retaining previously used GPU memory, which, in multi-tenant inference deployments, can expose sensitive tensor data from other...

CVE-2026-54236

A flaw was found in vLLM, an inference and serving engine for large language models LLMs. An unauthenticated attacker can exploit this vulnerability by sending specially crafted malformed image bytes through the Anthropic Messages API. This action causes an error message to be generated that...

CVE-2026-54232

A flaw was found in vLLM, an inference and serving engine for large language models LLMs. This vulnerability, a dependency confusion attack, allows a remote attacker to execute arbitrary code with root privileges during the Docker build process. By exploiting this, an attacker can compromise the...

CVE-2026-48746

A flaw was found in vLLM, an inference and serving engine for large language models LLMs. This vulnerability, residing in ASGI web servers and Starlette's trust in them, allows an attacker to bypass the OpenAI API Authentication Middleware. This bypass enables unauthorized access to the API witho...

CVE-2026-48746 vulnerabilities

Vulnerabilities for packages: py3-vllm-cuda-12.9, py3-vllm-cuda-12.4, py3-vllm-cuda-13.0, tritonserver-backend-vllm-cuda-13.0...

GHSA-94F4-HR76-P5J6 vulnerabilities

Vulnerabilities for packages: py3-vllm-cuda-12.9, py3-vllm-cuda-12.4, py3-vllm-cuda-13.0, tritonserver-backend-vllm-cuda-13.0...

GHSA-4XGF-CPJX-PC3J vulnerabilities

Vulnerabilities for packages: airflow-core, litellm, azureml-inference-server-http-fips, prefect-fips, tritonserver-backend-vllm-cuda-13.0, airflow, airflow-postgres-fips, open-webui, lmcache-cuda-12.8, semgrep, vllm-cuda-13.2, azureml-inference-server-http, prefect, vllm-openai-cuda-13.0,...

CVE-2026-47155

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...

CVE-2026-41523

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.22.0, an assert-based security check in vLLM's activation function loading allows any unauthenticated attacker to achieve arbitrary code execution on the server by publishing a malicious HuggingFace model, when vLL...

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-2026-41523 vLLM: Security Check Bypass via assert Statement in Activation Function Loading Allows Arbitrary Code Execution

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.22.0, an assert-based security check in vLLM's activation function loading allows any unauthenticated attacker to achieve arbitrary code execution on the server by publishing a malicious HuggingFace model, when vLL...

CVE-2026-41523

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.22.0, an assert-based security check in vLLM's activation function loading allows any unauthenticated attacker to achieve arbitrary code execution on the server by publishing a malicious HuggingFace model, when vLL...

CVE-2026-41523

vLLM prior to 0.22.0 is affected by an assert-based security check in the activation function loading that can permit arbitrary code execution when a malicious HuggingFace model is loaded and vLLM runs in Python optimized mode. The attacker-controlled inputs are the activation function names from...

CVE-2026-41523 vLLM: Security Check Bypass via assert Statement in Activation Function Loading Allows Arbitrary Code Execution

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.22.0, an assert-based security check in vLLM's activation function loading allows any unauthenticated attacker to achieve arbitrary code execution on the server by publishing a malicious HuggingFace model, when vLL...

CVE-2026-54232

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.22.1, the vLLM Dockerfile is vulnerable to a dependency confusion attack through the flashinfer-jit-cache package. The package is installed from a custom index flashinfer.ai/whl/ using --extra-index-url, but the...

CVE-2026-54232 vLLM: Dependency Confusion Vulnerability in vLLM Dockerfile

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.22.1, the vLLM Dockerfile is vulnerable to a dependency confusion attack through the flashinfer-jit-cache package. The package is installed from a custom index flashinfer.ai/whl/ using --extra-index-url, but the...

CVE-2026-54232 vLLM: Dependency Confusion Vulnerability in vLLM Dockerfile

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.22.1, the vLLM Dockerfile is vulnerable to a dependency confusion attack through the flashinfer-jit-cache package. The package is installed from a custom index flashinfer.ai/whl/ using --extra-index-url, but the...