Vulnerability 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.
CVSS Score
LOW
Affected Products
| Vendor | Product | Versions |
|---|---|---|
| Vllm | Vllm | < 0.7.2 |
Related Weaknesses (CWE)
References
- https://github.com/python/cpython/commit/432117cd1f59c76d97da2eaff55a7d758301dbcNot Applicable
- https://github.com/vllm-project/vllm/pull/12621Issue Tracking
- https://github.com/vllm-project/vllm/security/advisories/GHSA-rm76-4mrf-v9r8Vendor Advisory
FAQ
What is CVE-2025-25183?
CVE-2025-25183 is a vulnerability with a CVSS score of 2.6 (LOW). 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...
How severe is CVE-2025-25183?
CVE-2025-25183 has been rated LOW with a CVSS base score of 2.6/10. Review the CVSS metrics above for detailed severity breakdown.
Is there a patch for CVE-2025-25183?
Check the references section above for vendor advisories and patch information. Affected products include: Vllm Vllm.