Openshift

A flaw was found in ose-openshift-apiserver. This vulnerability allows internal network enumeration, service discovery, limited information disclosure, and potential denial-of-service (DoS) through Server-Side Request Forgery (SSRF) due to missing IP address and network-range validation when processing user-supplied image references.

A vulnerability in the assisted-service REST API, an optional Assisted Installer (assisted-service) component in the Multicluster Engine (MCE), allows an authenticated user with minimal namespace-scoped privileges to obtain administrative credentials for arbitrary clusters provisioned through the hub. The credentials download endpoint (GET /v2/clusters/{cluster_id}/credentials, which returns the kubeadmin password) and the kubeconfig download endpoint are operational in AUTH_TYPE=local mode, the only authentication mode available in on-premises ACM/MCE hub deployments. The local authenticator unconditionally grants full administrative access to any request bearing a valid JWT, with no per-endpoint restrictions. A valid local JWT is embedded as a plaintext query parameter in InfraEnvStatus.ISODownloadURL and is readable by any user who has get rights on an InfraEnv object in their own namespace. The affected components ship as part of Multicluster Engine (MCE). The Red Hat Advanced Cluster Management (ACM) deployments that include MCE are equally affected. This issue does not affect the hosted SaaS offering (console.redhat.com), which uses a different authentication mode. Successful exploitation gives the attacker the kubeadmin password and kubeconfig for any OpenShift cluster provisioned through the affected hub, granting unrestricted root-level administrative access to those spoke clusters.

OpenSift is an AI study tool that sifts through large datasets using semantic search and generative AI. Prior to version 1.6.3-alpha, the URL ingest pipeline accepted user-controlled remote URLs with incomplete destination restrictions. Although private/local host checks existed, missing restrictions for credentialed URLs, non-standard ports, and cross-host redirects left SSRF-class abuse paths in non-localhost deployments. This issue has been patched in version 1.6.3-alpha.

OpenSift is an AI study tool that sifts through large datasets using semantic search and generative AI. Prior to version 1.6.3-alpha, multiple storage helpers used path construction patterns that did not uniformly enforce base-directory containment. This created path-injection risk in file read/write/delete flows if malicious path-like values were introduced. This issue has been patched in version 1.6.3-alpha.

OpenSift is an AI study tool that sifts through large datasets using semantic search and generative AI. Prior to version 1.6.3-alpha, some endpoints returned raw exception strings to clients. Additionally, login token material was exposed in UI/rendered responses and token rotation output. This issue has been patched in version 1.6.3-alpha.

OpenSift is an AI study tool that sifts through large datasets using semantic search and generative AI. Versions 1.1.2-alpha and below, use non-atomic and insufficiently synchronized local JSON persistence flows, potentially causing concurrent operations to lose updates or corrupt local state across sessions/study/quiz/flashcard/wellness/auth stores. This issue has been fixed in version 1.1.3-alpha.

OpenSift is an AI study tool that sifts through large datasets using semantic search and generative AI. In versions 1.1.2-alpha and below, URL ingest allows overly permissive server-side fetch behavior and can be coerced into requesting unsafe targets. Potential access/probing of private/local network resources from the OpenSift host process when ingesting attacker-controlled URLs. This issue has been fixed in version 1.1.3-alpha. To workaround when using trusted local-only exceptions, use OPENSIFT_ALLOW_PRIVATE_URLS=true with caution.

OpenSift is an AI study tool that sifts through large datasets using semantic search and generative AI. Versions 1.1.2-alpha and below render untrusted user/model content in chat tool UI surfaces using unsafe HTML interpolation patterns, leading to XSS. Stored content can execute JavaScript when later viewed in authenticated sessions. An attacker who can influence stored study/quiz/flashcard content could trigger script execution in a victim’s browser, potentially performing actions as that user in the local app session. This issue has been fixed in version 1.1.3-alpha.