CVE-2026-15154 in OpenShift AI
Summary
by MITRE • 07/08/2026
A flaw was found in `guardrails-detectors`, a component of Red Hat OpenShift AI. This vulnerability, known as Regular Expression Denial of Service (ReDoS), allows a remote attacker to provide specially crafted regular expressions to the public detection API. This can cause catastrophic backtracking, leading to a worker process consuming 100% CPU indefinitely and resulting in a denial of service for the entire guardrails-mediated LLM pipeline.
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Analysis
by VulDB Data Team • 07/08/2026
The vulnerability identified in guardrails-detectors within Red Hat OpenShift AI represents a critical security flaw that undermines the availability and reliability of large language model processing pipelines. This component serves as a crucial mediation layer for detecting and filtering content within AI workloads, making it a prime target for attackers seeking to disrupt service operations. The flaw manifests specifically through improper handling of regular expressions in the public detection API, where untrusted input can trigger malicious patterns that exploit fundamental weaknesses in regex matching algorithms.
The technical implementation of this vulnerability stems from the inherent nature of regular expression engines that perform backtracking when processing certain inputs. When an attacker crafts malicious regular expressions containing nested quantifiers or alternation patterns, the regex engine can spend exponentially increasing amounts of time attempting to match these inputs. This behavior creates a catastrophic backtracking scenario where the computational complexity grows rapidly with input length, effectively consuming all available CPU resources within the worker process. The vulnerability impacts the core functionality of the guardrails-mediated pipeline by causing individual worker processes to become unresponsive and consume 100% CPU indefinitely.
From an operational perspective, this vulnerability poses significant risks to production environments utilizing Red Hat OpenShift AI for enterprise AI workloads. The denial of service condition affects not just individual API calls but entire processing pipelines that depend on the guardrails component for content filtering and safety mediation. Systems may experience complete unresponsiveness or severe performance degradation during attack scenarios, potentially disrupting critical business operations that rely on AI services. The impact extends beyond immediate service disruption to include potential cascading failures within containerized environments where resource constraints can affect adjacent services.
The security implications align with CWE-400, which classifies the vulnerability as a Regular Expression Denial of Service, and maps to ATT&CK technique T1499.004 for Network Denial of Service. Organizations utilizing this component face potential exposure to both automated scanning tools and targeted attacks seeking to exploit service availability. The vulnerability's remote nature means that attackers need only submit malicious inputs through the public API endpoint to trigger the denial of service condition, requiring no local system access or elevated privileges.
Mitigation strategies should focus on implementing input validation and sanitization for all regular expression patterns within the detection API. Organizations should consider rate limiting and resource quotas for API endpoints to prevent single requests from consuming excessive computational resources. The implementation of regex engine timeouts and maximum backtracking limits provides effective protection against catastrophic backtracking scenarios. Additionally, regular expression pattern compilation with safe algorithms that avoid exponential backtracking behaviors should be prioritized during development and security auditing processes. System monitoring should include detection of sustained high CPU usage patterns that may indicate exploitation attempts.