CVE-2024-8768 in vLLM
Summary
by MITRE • 09/17/2024
A flaw was found in the vLLM library. A completions API request with an empty prompt will crash the vLLM API server, resulting in a denial of service.
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Analysis
by VulDB Data Team • 09/18/2024
The vulnerability identified as CVE-2024-8768 resides within the vLLM library, a high-performance inference engine designed for large language models. This flaw manifests when the completions API receives a request containing an empty prompt parameter, causing the API server to experience a critical failure that results in complete service disruption. The issue represents a fundamental lack of input validation within the library's API handling mechanisms, specifically affecting how the system processes edge cases during prompt processing operations.
The technical root cause of this vulnerability stems from insufficient sanitization and validation of user-provided input parameters within the vLLM API server implementation. When an empty string is passed as a prompt value, the underlying processing logic fails to properly handle this null or empty state, leading to an unhandled exception that terminates the server process. This behavior aligns with CWE-20, which addresses improper input validation, and specifically demonstrates a failure in robust error handling for malformed or empty inputs. The vulnerability operates at the application layer and affects the API server's ability to maintain continuous operation under adversarial input conditions.
The operational impact of CVE-2024-8768 extends beyond simple service interruption to potentially compromise the availability of critical AI inference services. Organizations relying on vLLM for production deployments face significant risk of denial of service attacks that can be executed with minimal effort by submitting empty prompt requests. This vulnerability can be exploited by malicious actors to disrupt AI-powered applications, potentially affecting customer-facing services, automated decision-making systems, and enterprise AI workloads that depend on continuous model inference capabilities. The attack surface is particularly concerning given the widespread adoption of vLLM in production environments and the relative ease with which this denial of service condition can be triggered.
Mitigation strategies for this vulnerability should focus on implementing comprehensive input validation mechanisms within the vLLM library and associated API servers. Organizations should deploy immediate patches or updates that include proper null checking and empty string handling for prompt parameters before processing. Additionally, implementing request rate limiting and input sanitization middleware can provide defense-in-depth measures against potential abuse. From an ATT&CK framework perspective, this vulnerability maps to T1499.004 which covers network denial of service attacks, and organizations should consider implementing monitoring and alerting for unusual API request patterns that might indicate exploitation attempts. The recommended approach includes validating all user inputs at the API boundary and implementing graceful error handling that prevents server crashes while maintaining service availability for legitimate requests.