Red Hat AI Inference Server: Server-Side Request Forgery
CVE-2025-6242 is a high-severity server-side request forgery vulnerability in Red Hat AI Inference Server from Red Hat, with a CVSS 3.1 base score of 7.1 out of 10. It can be exploited remotely over the network by an attacker with a low-privileged account, without any action from a user. There is no confirmed exploitation so far. EPSS estimates a 0.25% chance of exploitation within 30 days, which is low compared with other CVEs.
High severity with no evidence of exploitation so far.
What to do
- Check which version of Red Hat AI Inference Server you run against the affected versions below, and install the vendor's latest release.
Affected products and versions
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| Red Hat | Red Hat AI Inference Server | Not specified in the record | Not stated |
| Red Hat | Red Hat Enterprise Linux AI (RHEL AI) | Not specified in the record | Not stated |
Version data comes from the organisation that assigned the CVE. Confirm against the vendor advisory before deciding you are unaffected.
How the severity score breaks down
- Attack vector
- NetworkCan be exploited remotely over a network, including the internet.
- Attack complexity
- HighSuccess depends on conditions outside the attacker's control.
- Privileges required
- LowA basic user account is enough.
- User interaction
- NoneNo action by a victim is required.
- Scope
- UnchangedImpact stays within the vulnerable component.
- Confidentiality
- HighAll data handled by the component can be read.
- Integrity
- LowLimited modification of data is possible.
- Availability
- HighThe component can be taken down completely.
Vector: CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:L/A:H. Scored by CNA (redhat.com).
Official 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.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Server-Side Request Forgery
The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination.
How developers prevent this class of flaw
- If there is a known list of applications, IP, hostname, and/or domains to which the product is allowed to communicate, then use allowlists to ensure that outgoing requests are restricted to these known entities. The allowlists could be in the application code or possibly enforced by firewalls or network segmentation.
- If the product is expected to be able to connect to arbitrary IP addresses or domains, consider using a denylist that includes private network addresses, internal domain names, etc. The denylist could be in the application code or possibly enforced by firewalls or network segmentation.
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: none, automatable: no, technical impact: partial).
- Record last changed materially (score, affected products, status or references).
References
Other references
Other vulnerabilities in Red Hat AI Inference Server
- 7.6 High severity
- 8.6 High severity
- 8.1 High severity
- 9.1 Critical severity
- 7.5 High severity
- 7.5 High severity
Recent Server-Side Request Forgery vulnerabilities
- 8.1 High severity
- 7.3 High severity
- 8.8 High severity
- 8.6 High severity
- 8.6 High severity
- 8.6 High severity
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