CVE-2025-48943
Vllm 0.8.0 – 0.9.0
Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:HSummary
CVE-2025-48943 is a medium-severity Uncaught Exception (CWE-248) vulnerability in Vllm Vllm. Its CVSS base score is 6.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Endpoint Denial of Service (T1499); ranked at the 35th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as NLP and Transformers; in the LLM/Generative AI Risks risk domain.
The strongest mitigations our analysis identified map to SA-8 (Security and Privacy Engineering Principles) and SC-24 (Fail in Known State) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-16515
Vulnerability Data
vLLM is an inference and serving engine for large language models (LLMs). Version 0.8.0 up to but excluding 0.9.0 have a Denial of Service (ReDoS) that causes the vLLM server to crash if an invalid regex was provided while using…
more
structured output. This vulnerability is similar to GHSA-6qc9-v4r8-22xg/CVE-2025-48942, but for regex instead of a JSON schema. Version 0.9.0 fixes the issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- NLP and Transformers
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: llms, vllm
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Security engineering principles include robust exception management to keep the system in a defined state.
Fail-in-known-state reduces the impact when an uncaught exception occurs by preserving a safe condition.
Error handling requirements force structured catching and response to exceptions instead of allowing them to propagate uncaught.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices explicitly require structured exception handling to prevent uncaught exceptions from reaching production.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing can detect uncaught exceptions before production deployment.
Secure development lifecycle includes exception-handling standards that reduce uncaught exceptions.
Application security requirements typically mandate robust error and exception handling.
Secure architecture principles call for centralized, comprehensive exception management.
Secure coding standards directly require catching and handling exceptions to prevent crashes or leaks.