CVE-2026-17557
Lm-evaluation-harness: lm-evaluation-harness: ssrf via job-spec model.url with bearer credential forwarding
A flaw was found in the lm-evaluation-harness component of Red Hat OpenShift AI. An authenticated user, able to submit an evaluation job, could exploit this by providing a malicious model URL in the JobSpec. This vulnerability allows for the disclosure of the tenant's model authentication secret to an attacker-controlled external listener. Additionally, it enables Server-Side Request Forgery (SSRF) attacks against internal network endpoints reachable from the evaluation job pod.
Attack profile
How easy it is to exploit
- Attack vector
- Network
- Complexity
- Low
- Privileges needed
- Low
- User interaction
- None
What an attacker can affect
- Confidentiality
- High
- Integrity
- None
- Availability
- None
Exploit likelihood
What each source reports
CVE List Score used
Lm-evaluation-harness: lm-evaluation-harness: ssrf via job-spec model.url with bearer credential forwarding
A flaw was found in the lm-evaluation-harness component of Red Hat OpenShift AI. An authenticated user, able to submit an evaluation job, could exploit this by providing a malicious model URL in the JobSpec. This vulnerability allows for the disclosure of the tenant's model authentication secret to an attacker-controlled external listener. Additionally, it enables Server-Side Request Forgery (SSRF) attacks against internal network endpoints reachable from the evaluation job pod.
- Record
CVE-2026-17557- CVSS 3.1
- 6.5 Medium
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N - Weakness
- CWE-918
- Updated
- 2026-11-02
EUVDEUVD-2026-107135
A flaw was found in the lm-evaluation-harness component of Red Hat OpenShift AI. An authenticated user, able to submit an evaluation job, could exploit this by providing a malicious model URL in the JobSpec. This vulnerability allows for the disclosure of the tenant's model authentication secret to an attacker-controlled external listener. Additionally, it enables Server-Side Request Forgery (SSRF) attacks against internal network endpoints reachable from the evaluation job pod.
- Record
EUVD-2026-107135- CVSS 3.1
- 6.5 Medium
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N - Updated
- 2026-11-02
GitHub AdvisoriesGHSA-xphj-jg67-vf2w
A flaw was found in the `lm-evaluation-harness` component of Red Hat OpenShift AI. An...
A flaw was found in the lm-evaluation-harness component of Red Hat OpenShift AI. An authenticated user, able to submit an evaluation job, could exploit this by providing a malicious model URL in the JobSpec. This vulnerability allows for the disclosure of the tenant's model authentication secret to an attacker-controlled external listener. Additionally, it enables Server-Side Request Forgery (SSRF) attacks against internal network endpoints reachable from the evaluation job pod.
- Record
GHSA-xphj-jg67-vf2w- CVSS 3.1
- 6.5 Medium
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N - Weakness
- CWE-918
- Updated
- 2026-11-02
NVD
A flaw was found in the lm-evaluation-harness component of Red Hat OpenShift AI. An authenticated user, able to submit an evaluation job, could exploit this by providing a malicious model URL in the JobSpec. This vulnerability allows for the disclosure of the tenant's model authentication secret to an attacker-controlled external listener. Additionally, it enables Server-Side Request Forgery (SSRF) attacks against internal network endpoints reachable from the evaluation job pod.
- Record
CVE-2026-17557- CVSS 3.1
- 6.5 Medium
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N - Weakness
- CWE-918
- Updated
- 2026-11-02
Affected products
History
| Recorded | Changed | Sources |
|---|---|---|
| first indexed | CVE List, EUVD, GitHub Advisories, NVD |
Dates are when Every CVE recorded each change. Upstream dates may differ.
In short
CVE-2026-17557 is a medium vulnerability (CVSS 6.5) in Red Hat Openshift Ai (rhoai), published October 21, 2026. No source lists public exploit code. EPSS puts the chance of exploitation in the next 30 days at 0.33%, the 26th percentile. No source names a fixed version.