CVE-2025-59425 identifies a covert timing channel vulnerability (CWE-385) in the vLLM project, an inference and serving engine for large language models. Prior to version 0.11.0rc2, the API key validation process uses a string comparison method that leaks timing information proportional to the number of correct characters matched in the provided key. This timing discrepancy enables an attacker to perform a side-channel attack by measuring response times across multiple attempts, gradually reconstructing the valid API key character-by-character. Because the validation occurs without requiring authentication or user interaction, the attack can be executed remotely over the network. Successful exploitation results in authentication bypass, granting unauthorized access to the vLLM service, potentially exposing sensitive model inference capabilities or data. The vulnerability does not impact integrity or availability but compromises confidentiality by exposing authentication credentials. The issue is resolved in vLLM version 0.11.0rc2 by implementing a constant-time comparison method for API key validation, eliminating timing leaks. No known exploits are reported in the wild yet, but the vulnerability's nature and ease of exploitation make it a significant risk for deployments relying on vLLM's built-in API key validation.

For European organizations, this vulnerability poses a significant risk to the confidentiality of API keys protecting access to vLLM inference services. Unauthorized access could lead to misuse of AI model inference capabilities, data leakage, or unauthorized query execution. Organizations deploying vLLM in cloud or on-premises environments without additional API key protection mechanisms are particularly vulnerable. The attack requires no privileges or user interaction and can be executed remotely, increasing the threat surface. Given the growing adoption of AI and LLM technologies in sectors such as finance, healthcare, and government across Europe, exploitation could lead to exposure of sensitive data or disruption of AI-powered services. Although integrity and availability are not directly impacted, the breach of authentication could facilitate further attacks or unauthorized data access. The absence of known exploits in the wild provides a window for proactive mitigation, but the high CVSS score underscores the urgency of patching.

European organizations should immediately upgrade all vLLM deployments to version 0.11.0rc2 or later to eliminate the timing attack vulnerability. Where immediate upgrade is not feasible, implement compensating controls such as network-level restrictions to limit access to the API key validation endpoint, and deploy rate limiting to reduce the feasibility of timing analysis attacks. Consider integrating external authentication and authorization mechanisms that do not rely solely on vLLM's built-in API key validation. Employ monitoring and anomaly detection to identify unusual access patterns indicative of timing attacks or brute-force attempts. Developers should audit any custom API key validation code to ensure constant-time comparison methods are used. Additionally, organizations should review and rotate API keys regularly to minimize exposure. Finally, educate security teams about timing attacks and side-channel vulnerabilities to improve detection and response capabilities.