CVE-2025-25183

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.

The following products are affected by CVE-2025-25183 vulnerability.

Here, you will find a curated list of external links that provide in-depth information to CVE-2025-25183.

URL	Resource
https://github.com/python/cpython/commit/432117cd1f59c76d97da2eaff55a7d758301dbc7
https://github.com/python/cpython/pull/99541
https://github.com/vllm-project/vllm
https://github.com/vllm-project/vllm/commit/73b35cca7f3745d07d439c197768b25d88b6ab7f
https://github.com/vllm-project/vllm/pull/12621
https://github.com/vllm-project/vllm/security/advisories/GHSA-rm76-4mrf-v9r8
https://nvd.nist.gov/vuln/detail/CVE-2025-25183
https://www.cve.org/CVERecord?id=CVE-2025-25183