CVE-2025-6242: Red Hat AI Inference Server · Server-Side Request Forgery (SSRF)
Affects Red Hat AI Inference ServerSource CVE Database V5
Not known to be exploited · CVSS 7.1 High.
Data as of 25 Oct 2026, 19:02 UTC
- EPSS
- 0.2% chance of exploitation in 30 days – higher than 15% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:L/A:HExploitation evidence
Disclosed: evidence found.
CVE published 7 Oct 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-25.
What to do
AI-suggested
To mitigate CVE-2025-6242 effectively, organizations should implement a multi-layered approach beyond generic patching advice. First, monitor Red Hat's official channels closely for patches or updates addressing this vulnerability and apply them promptly once available. In the interim, restrict network egress from the AI Inference Server to only trusted external endpoints using firewall rules or network segmentation to prevent arbitrary outbound requests. Implement strict input validation and URL allowlisting at the application level to limit URLs accepted by load_from_url and load_from_url_async methods. Employ web application firewalls (WAFs) capable of detecting and blocking SSRF patterns targeting internal IP ranges. Conduct internal network scanning to identify and secure sensitive services that could be targeted via SSRF. Enable detailed logging and alerting on unusual outbound requests from the AI server to detect potential exploitation attempts early. Finally, review and minimize privileges assigned to the AI Inference Server process to limit the impact of a successful attack and consider deploying runtime application self-protection (RASP) solutions to detect anomalous behaviors in real time.
Description
A Server-Side Request Forgery (SSRF) vulnerability exists in the MediaConnector class within the vLLM project's multimodal feature set. The load_from_url and load_from_url_async methods fetch and process media from user-provided URLs without adequate restrictions on the target hosts. This allows an attacker to coerce the vLLM server into making arbitrary requests to internal network resources.