CVE-2026-73556 in vLLM
Summary
by MITRE • 08/13/2026
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.
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Analysis
by VulDB Data Team • 08/13/2026
The vulnerability identified in vLLM versions prior to 026.0 represents a critical security flaw in the structured output processing mechanism that directly impacts the engine's performance and resource utilization. This issue resides within the lm-format-enforcer backend where the structured_outputs.regex parameter is handled without proper input validation or sanitization, creating a pathway for malicious actors to exploit the system through carefully crafted regular expressions. The vulnerability specifically affects the /v1/completions endpoint which serves as the primary interface for inference requests, making it accessible to unauthenticated users who can leverage this weakness to disrupt normal operations.
The technical implementation flaw stems from the direct pass-through of user-provided regex patterns to the lmformatenforcer.RegexParser component without any form of compilation timeout handling or validation routines. This design decision allows attackers to submit malformed or computationally expensive regular expressions that trigger catastrophic backtracking behavior in the underlying regex engine. The absence of compile_regex_with_timeout function calls and proper validate_structured_output_request_lm_format_enforcer checks creates an environment where resource exhaustion becomes possible through simple API requests. When such malicious patterns are processed, they consume CPU cycles indefinitely, causing the structured-output engine path to stall completely while consuming a full CPU core.
The operational impact of this vulnerability extends beyond simple denial-of-service scenarios as it fundamentally compromises the reliability and performance of the vLLM inference engine. Attackers can leverage this weakness to cause sustained resource exhaustion attacks that prevent legitimate requests from being processed, effectively creating a service disruption for all users of the system. The stall condition affects not just individual requests but entire processing pipelines within the structured output engine, potentially causing cascading failures throughout the system architecture. This vulnerability represents a significant risk to production environments where vLLM serves as a critical component for large language model inference services.
This security issue maps directly to CWE-400, which covers "Uncontrolled Resource Consumption" and specifically addresses the lack of input validation and resource management in regular expression processing. The vulnerability also aligns with ATT&CK technique T1499.004, "Resource Hijacking," where adversaries consume system resources to disrupt service availability. Additionally, it demonstrates characteristics of CWE-326, "Inadequate Encryption Strength," in the context of resource management rather than encryption, as the lack of proper input sanitization allows attackers to manipulate system resources through seemingly benign API interactions.
The mitigation strategy involves upgrading to vLLM version 0.26.0 or later where the fix has been implemented to properly validate and sanitize regex patterns before processing them through the lm-format-enforcer backend. Organizations should also implement rate limiting and resource monitoring at the API gateway level to detect unusual CPU consumption patterns that may indicate exploitation attempts. Additionally, network segmentation and access controls should be enforced to limit exposure of the vulnerable endpoint to untrusted users or systems. Regular security audits of input validation mechanisms and comprehensive testing of regex processing components should be conducted to prevent similar issues from emerging in other parts of the system architecture.