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| CVE-ID | ||
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CVE-2025-25183 |
• CVSS Severity Rating • Fix Information • Vulnerable Software Versions • SCAP Mappings • CPE Information
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| 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. | ||
| References | ||
| Note: References are provided for the convenience of the reader to help distinguish between vulnerabilities. The list is not intended to be complete. | ||
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| Assigning CNA | ||
| GitHub (maintainer security advisories) | ||
| Date Record Created | ||
| 20250203 | Disclaimer: The record creation date may reflect when the CVE ID was allocated or reserved, and does not necessarily indicate when this vulnerability was discovered, shared with the affected vendor, publicly disclosed, or updated in CVE. | |
| Phase (Legacy) | ||
| Assigned (20250203) | ||
| Votes (Legacy) | ||
| Comments (Legacy) | ||
| Proposed (Legacy) | ||
| N/A | ||
| This is an record on the CVE List, which provides common identifiers for publicly known cybersecurity vulnerabilities. | ||
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