Vllm

vLLM 0.17.0 contains a Server-Side Request Forgery (SSRF) vulnerability where inconsistent URL parsing between the validation layer (urllib3) and the HTTP client (aiohttp/yarl) allows authenticated attackers to bypass SSRF protections and make requests to internal resources. An attacker with valid credentials can craft malicious URLs to access restricted endpoints or internal services that should be blocked by the SSRF mitigation implemented in version 0.15.1.

Information exposure in vLLM inference engine versions 0.8.3 to before 0.14.1. Invalid image requests to the multimodal endpoint cause sensitive data logging. Patch available.

vLLM before version 0.14.1 contains a server-side request forgery vulnerability in the MediaConnector class where inconsistent URL parsing between libraries allows attackers to bypass host restrictions and force the server to make arbitrary requests to internal network resources. Public exploit code exists for this vulnerability, which poses significant risk in containerized environments where a compromised vLLM instance could be leveraged to access restricted internal systems. The vulnerability affects users running vLLM's multimodal features with untrusted input.

vLLM is an inference and serving engine for large language models (LLMs). [CVSS 8.8 HIGH]

Vllm versions up to 0.12.0 is affected by allocation of resources without limits or throttling (CVSS 6.5).

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 Nemotron_Nano_VL_Config. When vllm loads a model config that contains an auto_map entry, the config class resolves that mapping with get_class_from_dynamic_module(...) and immediately instantiates the returned class. This fetches and executes Python from the remote repository referenced in the auto_map string. Crucially, this happens even when the caller explicitly sets trust_remote_code=False in vllm.transformers_utils.config.get_config. In practice, an attacker can publish a benign-looking frontend repo whose config.json points via auto_map to a separate malicious backend repo; loading the frontend will silently run the backend’s code on the victim host. This vulnerability is fixed in 0.11.1.

vLLM is an inference and serving engine for large language models (LLMs). Rated medium severity (CVSS 6.5), this vulnerability is remotely exploitable, low attack complexity. This Allocation of Resources Without Limits vulnerability could allow attackers to exhaust system resources through uncontrolled allocation.

vLLM is an inference and serving engine for large language models (LLMs). Rated high severity (CVSS 8.3), this vulnerability is remotely exploitable, low attack complexity.

vLLM is an inference and serving engine for large language models (LLMs). Rated high severity (CVSS 8.8), this vulnerability is remotely exploitable, low attack complexity.

vLLM is an inference and serving engine for large language models (LLMs). Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. This Uncontrolled Resource Consumption vulnerability could allow attackers to cause denial of service by exhausting system resources.

vLLM is an inference and serving engine for large language models (LLMs). Rated medium severity (CVSS 6.5), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available and no vendor patch available.

vLLM is an inference and serving engine for large language models (LLMs). Rated medium severity (CVSS 6.5), this vulnerability is remotely exploitable, low attack complexity.

vLLM is an inference and serving engine for large language models (LLMs). Rated medium severity (CVSS 6.5), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available.

vLLM, an inference and serving engine for large language models (LLMs), has a Regular Expression Denial of Service (ReDoS) vulnerability in the file. Rated medium severity (CVSS 6.5), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available.

vLLM is an inference and serving engine for large language models (LLMs). Rated medium severity (CVSS 4.2), this vulnerability is remotely exploitable.

vLLM is an inference and serving engine for large language models (LLMs). Rated low severity (CVSS 2.6), this vulnerability is remotely exploitable.

vLLM, an inference and serving engine for large language models (LLMs), has an issue in versions 0.6.5 through 0.8.4 that ONLY impacts environments using the `PyNcclPipe` KV cache transfer. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.

vLLM is an inference and serving engine for large language models. Rated high severity (CVSS 8.0), this vulnerability is low attack complexity. No vendor patch available.

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated medium severity (CVSS 6.5), this vulnerability is remotely exploitable, low attack complexity. Public exploit code available and no vendor patch available.

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated critical severity (CVSS 10.0), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.

vllm-project vllm version 0.6.0 contains a vulnerability in the AsyncEngineRPCServer() RPC server entrypoints. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.

vllm-project vllm version v0.6.2 contains a vulnerability in the MessageQueue.dequeue() API function. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated critical severity (CVSS 9.0), this vulnerability is low attack complexity. This Deserialization of Untrusted Data vulnerability could allow attackers to execute arbitrary code through malicious serialized objects.

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated medium severity (CVSS 6.5), this vulnerability is remotely exploitable, low attack complexity. This Allocation of Resources Without Limits vulnerability could allow attackers to exhaust system resources through uncontrolled allocation.

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated low severity (CVSS 2.6), this vulnerability is remotely exploitable. No vendor patch available.

vLLM is a library for LLM inference and serving. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required. This Deserialization of Untrusted Data vulnerability could allow attackers to execute arbitrary code through malicious serialized objects.