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Vllm CVE-2025-30202

HIGH
Allocation of Resources Without Limits or Throttling (CWE-770)
2025-04-30 security-advisories@github.com GHSA-9f8f-2vmf-885j
7.5
CVSS 3.1 · GitHub Advisory
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Severity by source

GitHub Advisory PRIMARY
7.5 HIGH
AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Red Hat
7.5 HIGH
qualitative

Primary rating from GitHub Advisory.

CVSS VectorGitHub Advisory

Attack Vector
Network
Attack Complexity
Low
Privileges Required
None
User Interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

Lifecycle Timeline

4
Analysis Generated
Mar 28, 2026 - 18:39 vuln.today
Patch released
Mar 28, 2026 - 18:39 nvd
Patch available
PoC Detected
May 14, 2025 - 19:59 vuln.today
Public exploit code
CVE Published
Apr 30, 2025 - 01:15 nvd
HIGH 7.5

DescriptionGitHub Advisory

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.5.2 and prior to 0.8.5 are vulnerable to denial of service and data exposure via ZeroMQ on multi-node vLLM deployment. In a multi-node vLLM deployment, vLLM uses ZeroMQ for some multi-node communication purposes. The primary vLLM host opens an XPUB ZeroMQ socket and binds it to ALL interfaces. While the socket is always opened for a multi-node deployment, it is only used when doing tensor parallelism across multiple hosts. Any client with network access to this host can connect to this XPUB socket unless its port is blocked by a firewall. Once connected, these arbitrary clients will receive all of the same data broadcasted to all of the secondary vLLM hosts. This data is internal vLLM state information that is not useful to an attacker. By potentially connecting to this socket many times and not reading data published to them, an attacker can also cause a denial of service by slowing down or potentially blocking the publisher. This issue has been patched in version 0.8.5.

AnalysisAI

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.

Technical ContextAI

This vulnerability is classified as Allocation of Resources Without Limits (CWE-770), which allows attackers to exhaust system resources through uncontrolled allocation. vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Versions starting from 0.5.2 and prior to 0.8.5 are vulnerable to denial of service and data exposure via ZeroMQ on multi-node vLLM deployment. In a multi-node vLLM deployment, vLLM uses ZeroMQ for some multi-node communication purposes. The primary vLLM host opens an XPUB ZeroMQ socket and binds it to ALL interfaces. While the socket is always opened for a multi-node deployment, it is only used when doing tensor parallelism across multiple hosts. Any client with network access to this host can connect to this XPUB socket unless its port is blocked by a firewall. Once connected, these arbitrary clients will receive all of the same data broadcasted to all of the secondary vLLM hosts. This data is internal vLLM state information that is not useful to an attacker. By potentially connecting to this socket many times and not reading data published to them, an attacker can also cause a denial of service by slowing down or potentially blocking the publisher. This issue has been patched in version 0.8.5. Affected products include: Vllm. Version information: prior to 0.8.5.

RemediationAI

A vendor patch is available. Apply the latest security update as soon as possible. Set resource limits, implement rate limiting, validate input sizes.

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CVE-2025-30202 vulnerability details – vuln.today

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