CVE-2024-8939 in Enterprise Linux AI
Summary
by MITRE • 09/17/2024
A vulnerability was found in the ilab model serve component, where improper handling of the best_of parameter in the vllm JSON web API can lead to a Denial of Service (DoS). The API used for LLM-based sentence or chat completion accepts a best_of parameter to return the best completion from several options. When this parameter is set to a large value, the API does not handle timeouts or resource exhaustion properly, allowing an attacker to cause a DoS by consuming excessive system resources. This leads to the API becoming unresponsive, preventing legitimate users from accessing the service.
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Analysis
by VulDB Data Team • 08/31/2025
The vulnerability identified as CVE-2024-8939 resides within the ilab model serve component, specifically affecting the vllm JSON web API implementation. This flaw manifests in the improper handling of the best_of parameter which is designed to return the best completion from multiple generated options for large language model inference tasks. The parameter serves as a mechanism for users to request multiple potential responses and select the most suitable one, but when misconfigured or maliciously exploited, it creates a critical security risk. The vulnerability stems from inadequate resource management and timeout mechanisms within the API processing pipeline, where the system fails to enforce reasonable limits on parameter values that could lead to excessive computational demands.
The technical exploitation of this vulnerability occurs when an attacker submits a malicious request with an extremely large value for the best_of parameter. This parameter controls how many candidate completions the system generates before selecting the best one, and when set to unreasonable numbers, it triggers cascading resource consumption across CPU, memory, and potentially network resources. The API lacks proper input validation and resource throttling mechanisms that would normally prevent such excessive processing demands from overwhelming the system. This behavior aligns with CWE-400, which classifies unchecked resource consumption as a fundamental weakness in resource management, and can be categorized under the broader ATT&CK technique T1499.004 for network denial of service attacks. The absence of timeout controls and resource limits allows the system to continue processing indefinitely, consuming available computational resources until the service becomes completely unresponsive.
The operational impact of CVE-2024-8939 extends beyond simple service disruption to create significant business and security implications for organizations relying on LLM inference services. When exploited, this vulnerability can cause complete API unavailability, preventing legitimate users from accessing critical language model capabilities for tasks such as automated content generation, chatbot responses, or sentence completion services. The DoS condition affects not only the immediate availability of the service but also impacts system stability, potentially causing cascading failures in dependent applications that rely on the model serving infrastructure. Organizations may experience reduced productivity, customer dissatisfaction, and potential revenue loss due to service interruptions. The vulnerability particularly affects cloud-based LLM services and enterprise applications where multiple concurrent users might be accessing the API simultaneously, amplifying the impact of resource exhaustion attacks.
Mitigation strategies for CVE-2024-8939 should focus on implementing comprehensive input validation and resource limiting mechanisms within the API processing layer. Organizations should establish strict upper bounds for the best_of parameter, typically limiting it to reasonable values such as 1-10, depending on the system capacity and performance requirements. Implementing proper timeout mechanisms and resource consumption monitoring will help detect and prevent excessive processing demands before they overwhelm system resources. The solution should include rate limiting, connection pooling controls, and memory allocation limits to prevent unbounded resource consumption. Additionally, implementing circuit breaker patterns and health monitoring systems will enable automatic detection of resource exhaustion conditions and graceful degradation of service rather than complete failure. Security teams should also consider implementing API gateway controls that can enforce these limitations at the network boundary, providing an additional layer of protection against malicious parameter manipulation attacks. Regular security auditing of API endpoints and implementing proper logging mechanisms for monitoring parameter usage patterns will help identify potential exploitation attempts and ensure system resilience against similar resource exhaustion vulnerabilities.