- CVSS
- MEDIUM · 6.5 v3.1 CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
- Published
- 2025-05-30
- Weakness
- CWE-1333
- Source
- nvd.nist.gov/vuln/detail/CVE-2025-48887
Description
vLLM, an inference and serving engine for large language models (LLMs), has a Regular Expression Denial of Service (ReDoS) vulnerability in the file vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py of versions 0.6.4 up to but excluding 0.9.0. The root cause is the use of a highly complex and nested regular expression for tool call detection, which can be exploited by an attacker to cause severe performance degradation or make the service unavailable. The pattern contains multiple nested quantifiers, optional groups, and inner repetitions which make it vulnerable to catastrophic backtracking. Version 0.9.0 contains a patch for the issue.
References
How GTK Cyber trains on this
AI security training at GTK Cyber covers the LLM and ML-pipeline vulnerability classes that vulnerabilities like CVE-2025-48887 fall into. Our hands-on courses are taught by Charles Givre and other practitioners who break and defend production AI systems.