Resource exhaustion in Red Hat Enterprise Linux Ai (Rhel Ai)

CVE-2024-8939

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.

Vulnerability class: DoS (Denial of Service)

Published · last modified .

CVSS v3 metric

CVSS v3 base score 6.2 (Medium). Vector: CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H.

EPSS exploit prediction

EPSS: 0.002 (12.8th percentile), scored .

Very low probability of exploitation in the next 30 days; routine patching cadence is appropriate. 13th percentile — 12.8% of CVEs in the catalogue have a lower EPSS than this one. How to read EPSS.

EPSS trend (30 days)EPSS over the last 30 days for CVE-2024-8939: held from 0.002 to 0.002.

EPSS over last 30 days · oldest: 0.002 · newest: 0.002 · change: 0.000

Affected products

Weakness classification (CWE)

Public proof-of-concept exploits

References

Frequently asked questions

What is CVE-2024-8939?
CVE-2024-8939 is a medium-severity vulnerability in Red Hat Enterprise Linux Ai (Rhel Ai), classified under Uncontrolled Resource Consumption. CVSS score: 6.2/10. Published 2024-09-17.
How severe is CVE-2024-8939?
Medium severity. CVSS v3 base score is 6.2 out of 10.
Is CVE-2024-8939 known to be exploited?
1 public proof-of-concept repositories are indexed. Not currently listed in the CISA KEV catalog.