Vllm
Monthly
Remote code execution in vLLM prior to 0.11.1 allows an unauthenticated remote attacker to run arbitrary Python on the host when the victim loads a crafted model configuration through vLLM's transformers_utils.config.get_config path. The flaw bypasses the documented trust_remote_code=False guard because Nemotron_Nano_VL_Config resolves an attacker-controlled auto_map entry via get_class_from_dynamic_module and instantiates the fetched class; exploitation therefore depends on the victim choosing to load a malicious or compromised Hugging Face model, making it a supply-chain or untrusted-model risk rather than an unsolicited server-side attack. No public exploit code or CISA KEV entry was identified in the provided sources at time of analysis, and EPSS is 0.21% (42nd percentile).
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.
Timing-based API key recovery in vLLM before 0.11.0rc2 can let remote attackers bypass bearer-token authentication on deployments that rely on vLLM's built-in API key validation for /v1 routes. The flaw is a non-constant-time string comparison in AuthenticationMiddleware; exploitation requires many carefully timed requests and is practical only when network latency and jitter are low enough not to mask the per-character signal, so the assessed severity is high confidentiality impact with high attack complexity (AC:H). Publicly available exploit code exists, EPSS is low at 0.57% (46th percentile), and there is no CISA KEV confirmation of active exploitation.
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.
Remote code execution in vLLM prior to 0.11.1 allows an unauthenticated remote attacker to run arbitrary Python on the host when the victim loads a crafted model configuration through vLLM's transformers_utils.config.get_config path. The flaw bypasses the documented trust_remote_code=False guard because Nemotron_Nano_VL_Config resolves an attacker-controlled auto_map entry via get_class_from_dynamic_module and instantiates the fetched class; exploitation therefore depends on the victim choosing to load a malicious or compromised Hugging Face model, making it a supply-chain or untrusted-model risk rather than an unsolicited server-side attack. No public exploit code or CISA KEV entry was identified in the provided sources at time of analysis, and EPSS is 0.21% (42nd percentile).
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.
Timing-based API key recovery in vLLM before 0.11.0rc2 can let remote attackers bypass bearer-token authentication on deployments that rely on vLLM's built-in API key validation for /v1 routes. The flaw is a non-constant-time string comparison in AuthenticationMiddleware; exploitation requires many carefully timed requests and is practical only when network latency and jitter are low enough not to mask the per-character signal, so the assessed severity is high confidentiality impact with high attack complexity (AC:H). Publicly available exploit code exists, EPSS is low at 0.57% (46th percentile), and there is no CISA KEV confirmation of active exploitation.
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.