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.

Classification

CVE ID: CVE-2025-25183

CVSS Base Severity: LOW

CVSS Base Score: 2.6

CVSS Vector: CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:L/A:N

Affected Products

Vendor: vllm-project

Product: vllm

Exploit Prediction Scoring System (EPSS)

EPSS Score: 0.05% (probability of being exploited)

EPSS Percentile: 18.35% (scored less or equal to compared to others)

EPSS Date: 2025-03-08 (when was this score calculated)

References

https://github.com/vllm-project/vllm/security/advisories/GHSA-rm76-4mrf-v9r8
https://github.com/vllm-project/vllm/pull/12621
https://github.com/python/cpython/commit/432117cd1f59c76d97da2eaff55a7d758301dbc7

Timeline