- CVSS
- MEDIUM · 5.3v3.1CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L
- Published
- 2026-08-13
- Weakness
- CWE-400, CWE-1333
- Source
- nvd.nist.gov/vuln/detail/CVE-2026-73556
Description
vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the structured_outputs.regex parameter in vllm/v1/structured_output/backend_lm_format_enforcer.py is passed to lmformatenforcer.RegexParser without compile_regex_with_timeout or validation in validate_structured_output_request_lm_format_enforcer, allowing an unauthenticated /v1/completions request against the lm-format-enforcer backend to consume a CPU core and stall the structured-output engine path with a catastrophic regular expression. This issue is fixed in version 0.26.0.
References
- https://github.com/vllm-project/vllm/commit/c9a788eedc412acceaa5112e0d44624b49841577
- https://github.com/vllm-project/vllm/pull/47595
- https://github.com/vllm-project/vllm/releases/tag/v0.26.0
- https://github.com/vllm-project/vllm/security/advisories/GHSA-48jh-3gj7-fg8v
- https://github.com/vllm-project/vllm/security/advisories/GHSA-48jh-3gj7-fg8v
How GTK Cyber trains on this
AI security training at GTK Cyber covers the LLM and ML-pipeline vulnerability classes that vulnerabilities like CVE-2026-73556 fall into. Our hands-on courses are taught by Charles Givre and other practitioners who break and defend production AI systems.