MEDIUM · 5.3

CVE-2026-73556

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 lmfor...

Vulnerability 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.

CVSS Score

5.3

MEDIUM

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L
Attack Vector
NETWORK
Attack Complexity
LOW
Privileges Required
NONE
User Interaction
NONE
Scope
UNCHANGED
Confidentiality
NONE
Integrity
NONE
Availability
LOW

Related Weaknesses (CWE)

References

FAQ

What is CVE-2026-73556?

CVE-2026-73556 is a vulnerability with a CVSS score of 5.3 (MEDIUM). 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 lmfor...

How severe is CVE-2026-73556?

CVE-2026-73556 has been rated MEDIUM with a CVSS base score of 5.3/10. Review the CVSS metrics above for detailed severity breakdown.

Is there a patch for CVE-2026-73556?

Check the references section above for vendor advisories and patch information. Review vendor security bulletins for remediation guidance.