Raw vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HSummary
CVE-2024-8939 is a medium-severity Uncontrolled Resource Consumption (CWE-400) vulnerability. Its CVSS base score is 6.2 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Application or System Exploitation (T1499.004); ranked at the 14th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as APIs and Models; in the LLM/Generative AI Risks risk domain; MITRE ATLAS techniques in scope: External Harms (AML.T0048).
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-2887
Vulnerability Data
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…
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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.
- CWE(s)
AI Security AnalysisAI
- AI Category
- APIs and Models
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- AI-specific weaknesses CR
- CWE-1434 — Unbounded best_of inference param on LLM API exhausts resources (CWE-1434).
Mapped by Cyber Resilience · not in NVD. Poisoning and extraction cases are routed to MITRE ATLAS instead of a synthetic CWE.- Classification Reason
- The vulnerability affects the ilab model serve component using vLLM JSON web API for LLM-based sentence or chat completion, which is a serving API for AI models, fitting the APIs and Models category.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability enables denial of service by exploiting improper handling of the 'best_of' parameter in the vLLM JSON web API, leading to resource exhaustion and service unresponsiveness.
MITRE ATLAS TechniquesAI
MITRE ATLAS techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Limiting concurrent sessions directly prevents uncontrolled resource consumption by capping the number of active sessions per user or account.
Analysis identifies uncontrolled resource consumption indicative of denial-of-service or abuse attempts.
Contingency plan testing includes resource exhaustion scenarios to verify recovery, making it harder for attackers to sustain exploits that cause uncontrolled consumption.
Updated contingency plans include current procedures to detect, contain, and recover from resource exhaustion, limiting an attacker's ability to sustain impact from uncontrolled consumption.
Alternate site allows resumption of operations if resource exhaustion at the primary site is exploited to cause unavailability.
Alternate telecommunications services enable resumption of essential functions when primary services become unavailable due to uncontrolled resource consumption.
The team can analyze and respond to resource exhaustion incidents, reducing the impact of attacks that exploit uncontrolled consumption weaknesses.
Timely maintenance support and spare parts enable rapid recovery from failures induced by uncontrolled resource consumption, shortening the impact window of denial-of-service attacks.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Explicitly requires monitoring and maintaining resource capacity, directly addressing uncontrolled consumption to preserve availability.
Continuous monitoring of computing resources can detect resource exhaustion but does not itself enforce allocation limits.
Resilience mechanisms such as avoiding single points of failure indirectly reduce impact of resource exhaustion.
Hardened configuration baselines can include resource quotas and limits that constrain consumption.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Resource-utilization monitoring and alerting on bottlenecks or overloads limits the impact of denial-of-service or resource-exhaustion attacks.
By continuously monitoring utilization, stress-testing peak loads, and maintaining documented plans to scale or throttle resources, the control directly limits an attacker’s ability to drive a system into uncontrolled resource exhaustion.
Pre-agreed severity-based prioritization and resource allocation during incident triage reduce the likelihood that an attacker-induced resource exhaustion will overwhelm the organization before corrective action is taken.
Business-continuity plans that include resource-management controls reduce the likelihood that an attacker can trigger uncontrolled resource consumption by forcing the system into a degraded or fallback state.
Defining RTOs and capacity requirements for ICT services during business-impact analysis forces organizations to provision sufficient resources and throttling mechanisms, reducing the likelihood that an attacker can induce denial-of-service through uncontrolled resource consumption.
Early notification of anomalous resource consumption or system malfunctions enables throttling or isolation before availability is lost.