What is the CVSS score of CVE-2024-7557?

CVE-2024-7557 has a CVSS 3.1 base score of 8.8 (High). CVSS vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H. EPSS exploitation probability: 0.1%.

Which versions of Kubernetes are affected by CVE-2024-7557?

Organizations using A vulnerability face significant risk from CVE-2024-7557 rated CVSS 8.8.

How to fix or mitigate CVE-2024-7557?

Within 7 days: Identify all affected systems and apply vendor patches promptly. Monitor vendor channels for patch availability.

Kubernetes CVE-2024-7557

HIGH

Authentication Bypass by Primary Weakness (CWE-305)

2024-08-12 secalert@redhat.com

Authentication Bypass Privilege Escalation Information Disclosure Kubernetes Red Hat

8.8

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

8.8 HIGH

AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

Attack Vector

Network

Attack Complexity

Low

Privileges Required

Low

User Interaction

None

Scope

Unchanged

Confidentiality

High

Integrity

High

Availability

High

Lifecycle Timeline

Analysis Generated

Mar 19, 2026 - 17:30 vuln.today

CVE Published

Aug 12, 2024 - 13:38 nvd

HIGH 8.8

DescriptionCVE.org

A vulnerability was found in OpenShift AI that allows for authentication bypass and privilege escalation across models within the same namespace. When deploying AI models, the UI provides the option to protect models with authentication. However, credentials from one model can be used to access other models and APIs within the same namespace. The exposed ServiceAccount tokens, visible in the UI, can be utilized with oc --token={token} to exploit the elevated view privileges associated with the ServiceAccount, leading to unauthorized access to additional resources.

AnalysisAI

A critical authentication bypass vulnerability exists in Red Hat OpenShift AI/OpenShift Data Science that allows attackers to escalate privileges and access multiple AI models within the same namespace using credentials from any single authenticated model. The vulnerability stems from ServiceAccount tokens being exposed in the UI, which can be exploited to gain unauthorized access to other models and APIs in the namespace with elevated view privileges. With an EPSS score of 0.10% and no current KEV listing, the vulnerability has relatively low real-world exploitation activity despite its high CVSS score of 8.8.

Technical ContextAI

The vulnerability affects Red Hat OpenShift AI (formerly OpenShift Data Science) as identified by CPE cpe:2.3:a:redhat:openshift_ai:-:*:*:*:*:*:*:* and cpe:2.3:a:redhat:openshift_data_science:-:*:*:*:*:*:*:*. The issue is classified as CWE-305 (Authentication Bypass by Primary Weakness), indicating a fundamental flaw in how the platform validates authentication tokens across different models within a namespace. When deploying AI models through the UI with authentication protection enabled, the system improperly shares ServiceAccount tokens across all models in the same namespace, effectively breaking the intended isolation between protected models.

RemediationAI

Apply the patch provided in the opendatahub-io/odh-dashboard GitHub repository pull request #3198 (https://github.com/opendatahub-io/odh-dashboard/pull/3198) which addresses the authentication bypass issue. Organizations should review the Red Hat security advisory at https://access.redhat.com/security/cve/CVE-2024-7557 for specific version updates and patch availability. As an immediate mitigation, limit access to the OpenShift AI UI to trusted users only, implement namespace-level RBAC policies to restrict ServiceAccount permissions, and monitor for unusual cross-model access patterns using OpenShift audit logs.