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VulnerabilityPublished Updated

CVE-2026-18618: Red Hat OpenShift AI 2.25 · Unthrottled Resource Allocation

Affects Red Hat OpenShift AI 2.25Source CVE Database V5

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Not known to be exploited · CVSS 7.5 High.

Data as of 8 Nov 2026, 23:24 UTC

CVSS 3.1 base score
7.5High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.8% chance of exploitation in 30 days – higher than 58% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 10 Aug 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq 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 8 Nov 2026.

What to do

Affected software

ProductAffectedFixed in
Red Hat OpenShift AI 2.25rhoai/odh-mlmd-grpc-server-rhel9Affected–Fixed in–
Red Hat OpenShift AI 3.3rhoai/odh-mlmd-grpc-server-rhel9Affected–Fixed in–
Red Hat OpenShift AI 3.4rhoai/odh-mlmd-grpc-server-rhel9Affected–Fixed in–
Red Hat OpenShift AI 3.4rhoai/odh-mlmd-grpc-server-rhel9Affected–Fixed in–
Red Hat OpenShift AI 3.5rhoai/odh-latency-predictor-prediction-rhel9Affected–Fixed in–
Red Hat OpenShift AI 3.5rhoai/odh-latency-predictor-training-rhel9Affected–Fixed in–

AI-suggested

To mitigate the risk, enforce strict network policies that limit access to the MLMD pod's port 8080. Only allow inbound connections from essential components such as KFP v2 driver pods and designated DSP components. This reduces the attack surface and limits potential exploitation by in-cluster attackers. Monitor the Red Hat advisory for any future updates or patches.

Description

A flaw was found in ml-metadata. The statically-linked gRPC stack in ml-metadata is outdated, making it vulnerable to known HTTP/2 denial of service (DoS) issues. An in-cluster attacker, with network access to the MLMD pod, could exploit these vulnerabilities by sending specially crafted HTTP/2 requests. This could lead to a denial of service by crashing the MLMD pod, disrupting all pipeline runs in the affected namespace.

References

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