Vllm
CVE-2025-30165
HIGH
Severity by source
AV:A/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Primary rating from GitHub Advisory.
CVSS VectorGitHub Advisory
CVSS:3.1/AV:A/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Lifecycle Timeline
3DescriptionGitHub Advisory
vLLM is an inference and serving engine for large language models. In a multi-node vLLM deployment using the V0 engine, vLLM uses ZeroMQ for some multi-node communication purposes. The secondary vLLM hosts open a SUB ZeroMQ socket and connect to an XPUB socket on the primary vLLM host. When data is received on this SUB socket, it is deserialized with pickle. This is unsafe, as it can be abused to execute code on a remote machine. Since the vulnerability exists in a client that connects to the primary vLLM host, this vulnerability serves as an escalation point. If the primary vLLM host is compromised, this vulnerability could be used to compromise the rest of the hosts in the vLLM deployment. Attackers could also use other means to exploit the vulnerability without requiring access to the primary vLLM host. One example would be the use of ARP cache poisoning to redirect traffic to a malicious endpoint used to deliver a payload with arbitrary code to execute on the target machine. Note that this issue only affects the V0 engine, which has been off by default since v0.8.0. Further, the issue only applies to a deployment using tensor parallelism across multiple hosts, which we do not expect to be a common deployment pattern. Since V0 is has been off by default since v0.8.0 and the fix is fairly invasive, the maintainers of vLLM have decided not to fix this issue. Instead, the maintainers recommend that users ensure their environment is on a secure network in case this pattern is in use. The V1 engine is not affected by this issue.
AnalysisAI
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.
Technical ContextAI
This vulnerability is classified as Deserialization of Untrusted Data (CWE-502), which allows attackers to execute arbitrary code through malicious serialized objects. vLLM is an inference and serving engine for large language models. In a multi-node vLLM deployment using the V0 engine, vLLM uses ZeroMQ for some multi-node communication purposes. The secondary vLLM hosts open a SUB ZeroMQ socket and connect to an XPUB socket on the primary vLLM host. When data is received on this SUB socket, it is deserialized with pickle. This is unsafe, as it can be abused to execute code on a remote machine. Since the vulnerability exists in a client that connects to the primary vLLM host, this vulnerability serves as an escalation point. If the primary vLLM host is compromised, this vulnerability could be used to compromise the rest of the hosts in the vLLM deployment. Attackers could also use other means to exploit the vulnerability without requiring access to the primary vLLM host. One example would be the use of ARP cache poisoning to redirect traffic to a malicious endpoint used to deliver a payload with arbitrary code to execute on the target machine. Note that this issue only affects the V0 engine, which has been off by default since v0.8.0. Further, the issue only applies to a deployment using tensor parallelism across multiple hosts, which we do not expect to be a common deployment pattern. Since V0 is has been off by default since v0.8.0 and the fix is fairly invasive, the maintainers of vLLM have decided not to fix this issue. Instead, the maintainers recommend that users ensure their environment is on a secure network in case this pattern is in use. The V1 engine is not affected by this issue. Affected products include: Vllm.
RemediationAI
No vendor patch is available at time of analysis. Monitor vendor advisories for updates. Avoid deserializing untrusted data. Use safe serialization formats (JSON). Implement integrity checks and type allowlists.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated critical severity (CVSS 10.0
vllm-project vllm version v0.6.2 contains a vulnerability in the MessageQueue.dequeue() API function. Rated critical sev
Information exposure in vLLM inference engine versions 0.8.3 to before 0.14.1. Invalid image requests to the multimodal
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated high severity (CVSS 7.5), th
vLLM before version 0.14.1 contains a server-side request forgery vulnerability in the MediaConnector class where incons
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated medium severity (CVSS 6.5),
Vllm versions up to 0.12.0 is affected by allocation of resources without limits or throttling (CVSS 6.5).
Remote code execution in vLLM 0.10.1 through 0.13.x lets an attacker who controls the model repository or path run arbit
Server-Side Request Forgery in vLLM's multimodal MediaConnector allows remote attackers to coerce the inference server i
Denial of service in vllm 0.19.0's OpenAI-compatible serving path allows remote unauthenticated attackers to exhaust sch
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Rated critical severity (CVSS 9.0)
Remote code execution in vLLM versions prior to 0.22.1 allows attackers to backdoor production LLM inference deployments
Same weakness CWE-502 – Deserialization of Untrusted Data
View allVendor StatusVendor
Share
External POC / Exploit Code
Leaving vuln.today