CVE-2025-34351 vulnerabilities

Vulnerabilities for packages: py3-vllm-cuda-12.4...

GHSA-GX77-XGC2-4888 vulnerabilities

Vulnerabilities for packages: py3-vllm-cuda-12.4...

EUVD-2025-200115

vLLM vulnerable to remote code execution via transformersutils/getconfig...

GHSA-8FR4-5Q9J-M8GM vLLM vulnerable to remote code execution via transformers_utils/get_config

Summary vllm has a critical remote code execution vector in a config class named NemotronNanoVLConfig. When vllm loads a model config that contains an automap entry, the config class resolves that mapping with getclassfromdynamicmodule... and immediately instantiates the returned class. This...

CVE-2025-66448

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.11.1, vllm has a critical remote code execution vector in a config class named NemotronNanoVLConfig. When vllm loads a model config that contains an automap entry, the config class resolves that mapping with...

Arbitrary Code Injection

Overview vllm is an A high-throughput and memory-efficient inference and serving engine for LLMs Affected versions of this package are vulnerable to Arbitrary Code Injection via the config class named NemotronNanoVLConfig. An attacker can execute arbitrary code on the host system by publishing a...

CVE-2025-66448

vLLM (prior to 0.11.1) contains a remote code execution vulnerability in Nemotron_Nano_VL_Config where, during model loading, an auto_map entry can cause get_class_from_dynamic_module to fetch and execute code from a remote repository, bypassing trust_remote_code checks. This can enable an attack...

CVE-2025-66448 vLLM vulnerable to remote code execution via transformers_utils/get_config

vLLM is an inference and serving engine for large language models LLMs. Prior to 0.11.1, vllm has a critical remote code execution vector in a config class named NemotronNanoVLConfig. When vllm loads a model config that contains an automap entry, the config class resolves that mapping with...

CVE-2025-62593 vulnerabilities

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

GHSA-Q279-JHRF-CC6V vulnerabilities

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

GHSA-69J4-GRXJ-J64P vulnerabilities

Vulnerabilities for packages: tritonserver-backend-vllm-cuda-12.9...

GHSA-MRW7-HF4F-83PF vulnerabilities

Vulnerabilities for packages: tritonserver-backend-vllm-cuda-12.9...

GHSA-PMQF-X6X8-P7QW vulnerabilities

Vulnerabilities for packages: tritonserver-backend-vllm-cuda-12.9...

CVE-2025-62164 vulnerabilities

Vulnerabilities for packages: tritonserver-backend-vllm-cuda-12.9...

CVE-2025-62372 vulnerabilities

Vulnerabilities for packages: tritonserver-backend-vllm-cuda-12.9...

CVE-2025-62426 vulnerabilities

Vulnerabilities for packages: tritonserver-backend-vllm-cuda-12.9...

The vulnerability of the vLLM library for working with large language models, related to unlimited resource distribution, allows a hacker to trigger a service failure.

The vulnerability of the vLLM library for working with large language models is related to the unlimited distribution of resources during the processing of the chattemplatekwargs parameter. Exploiting this vulnerability could allow a malicious actor to cause service failures...

Server-Side Request Forgery (SSRF)

vllm is vulnerable to Server-Side Request Forgery SSRF. The vulnerability is due to insufficient restrictions on user-supplied URLs in the MediaConnector class’s loadfromurl and loadfromurlasync methods, which allows an attacker to coerce the server into making arbitrary internal network requests...

CVE-2025-62164

vLLM is an inference and serving engine for large language models LLMs. From versions 0.10.2 to before 0.11.1, a memory corruption vulnerability could lead to a crash denial-of-service and potentially remote code execution RCE, exists in the Completions API endpoint. When processing user-supplied...

CVE-2025-62372

vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before 0.11.1, users can crash the vLLM engine serving multimodal models by passing multimodal embedding inputs with correct ndim but incorrect shape e.g. hidden dimension is wrong, regardless of whether...