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VULNERABILITIES

CVE-2025-25183 Details

Description

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. Maliciously constructed statements can lead to hash collisions, resulting in cache reuse, which can interfere with subsequent responses and cause unintended behavior. Prefix caching makes use of Python's built-in hash() function. As of Python 3.12, the behavior of hash(None) has changed to be a predictable constant value. This makes it more feasible that someone could try exploit hash collisions. The impact of a collision would be using cache that was generated using different content. Given knowledge of prompts in use and predictable hashing behavior, someone could intentionally populate the cache using a prompt known to collide with another prompt in use. This issue has been addressed in version 0.7.2 and all users are advised to upgrade. There are no known workarounds for this vulnerability.

Metrics

CVSS 3.x Severity and Vector Strings:

References to Advisories, Solutions, and Tools

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Weakness Enumeration

CWE-IDCWE NameSource
CWE-354Improper Validation of Integrity Check Value[email protected]

Affected Products

ProductVersions
vllm vllm
< 0.7.2

CPE

  • cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:*

Remediation

  • No remediation found in references.

Change History

4 change records found show changes


QUICK INFO

CVE Dictionary Entry:
CVE-2025-25183
NVD Published Date:
Feb 7, 2025
NVD Last Modified:
Jun 17, 2026
Source:
[email protected]
CVE-2025-25183 Details - Not Deferred