| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| A flaw was found in the Data Science Pipelines Operator. This vulnerability allows an unauthenticated attacker to derive sensitive credentials, such as MariaDB root/user passwords and MinIO access/secret keys, if they can access the MinIO Route or MariaDB Service. The flaw occurs because the operator uses a cryptographically weak pseudo-random number generator (PRNG) to generate these credentials, making them predictable. Successful exploitation could lead to unauthorized access to all pipeline artifacts and metadata, resulting in significant information disclosure. |
| A flaw was found in the maas-api and maas-controller ServiceAccounts within Red Hat OpenShift AI. These ServiceAccounts are granted cluster-wide permissions that exceed their operational requirements. An attacker who compromises the identity of these ServiceAccounts, either through a remote code execution vulnerability or by creating a malicious pod in the same namespace, could exploit these excessive permissions. This could lead to full cluster administrator privileges through the creation of new ClusterRoleBindings or the disclosure of sensitive information by accessing all secrets across the cluster. |
| A flaw was found in `guardrails-detectors`, a component of Red Hat OpenShift AI. This vulnerability, known as Regular Expression Denial of Service (ReDoS), allows a remote attacker to provide specially crafted regular expressions to the public detection API. This can cause catastrophic backtracking, leading to a worker process consuming 100% CPU indefinitely and resulting in a denial of service for the entire guardrails-mediated LLM pipeline. |
| A flaw was found in the RHOAI training-operator. This vulnerability allows a user with standard edit or admin roles in any Kubernetes namespace to escalate their privileges. Through the creation of training jobs, an attacker can impersonate service accounts, access the host filesystem, and potentially execute arbitrary code remotely. This issue arises from the aggregation of training job permissions onto native Kubernetes edit and admin ClusterRoles, coupled with unrestricted PodTemplateSpec passthrough. |
| A flaw was found in the Red Hat OpenShift AI (RHOAI) overlay for the training operator. The RHOAI overlay incorrectly aggregates `trainjobs` management permissions into the native Kubernetes `edit ClusterRole`. This allows any user with `edit ClusterRole` permissions in a namespace to create, modify, and delete `TrainJobs`. When combined with a separate vulnerability (TRN-01) that permits arbitrary pod configurations, a remote attacker with namespace editor privileges could exploit this to escalate privileges, potentially leading to arbitrary code execution. |
| A flaw was found in Feast. The system improperly deserializes user-defined functions (UDFs) stored in its registry, which are serialized using the 'dill' library. This allows a remote attacker to store a malicious UDF, leading to unauthenticated arbitrary code execution on the feature server in default configurations. An authenticated attacker can also achieve arbitrary code execution on the registry server by bypassing authorization checks during deserialization. This vulnerability can result in cross-tenant data access and lateral movement within the system. |
| A flaw was found in Data Science Pipelines (DSP). An attacker with namespace editor privileges can bypass security hardening by submitting a malicious Argo Workflow through the V1 API path. This allows the API server to create pods with elevated privileges, acting as a 'confused deputy' on behalf of the attacker. Successful exploitation grants the attacker node-root access, enabling arbitrary code execution and full control over the underlying node. |
| A flaw was found in the Data Science Pipelines Operator (DSPO). A namespace editor can exploit a vulnerability in the spec.database.customExtraParams field, which allows for the injection of dangerous parameters into the MySQL Data Source Name (DSN) string. By manipulating these parameters, an attacker can enable LOCAL INFILE functionality and exfiltrate sensitive files, such as the service account token, from the operator pod. This can lead to privilege escalation, allowing a namespace editor to gain cluster-admin privileges. |
| A flaw was found in the Data Science Pipelines Operator (DSPO). The operator's ClusterRole, which defines its permissions, includes extensive privileges beyond what is necessary for its operation. These excessive permissions, such as the ability to execute commands within pods and manage cluster-wide roles, could be exploited. If the DSPO pod were compromised, an attacker could leverage these privileges to gain full administrative control over the entire Kubernetes cluster. |
| A flaw was found in odh-dashboard, the web console component of Red Hat OpenShift AI (RHOAI). Due to incorrect network binding, a malicious actor within the cluster can bypass authentication and impersonate any user by providing an arbitrary access token. This allows an attacker to gain unauthorized access to the Kubernetes API, potentially leading to arbitrary code execution, privilege escalation, or information disclosure. |
| A flaw was found in the TrustyAI Service (TAS) deployment. This vulnerability allows any pod on the cluster network to bypass authentication and directly access the TAS backend API. An attacker can exploit this to read, tamper with, or delete monitoring data and configurations, and inject arbitrary data into the service, potentially disrupting tenant operations. |
| A flaw was found in the trustyai-service-operator's LMEvalJob controller. An authenticated user within the cluster can exploit this vulnerability by configuring a sidecar container to bypass existing security policies. This allows the user to enable and execute untrusted remote code, leading to arbitrary code execution within the cluster. |
| A flaw was found in the `guardrails-detectors` component. This vulnerability allows a remote attacker to perform a blind Server-Side Request Forgery (SSRF) by submitting a specially crafted XML Schema Definition (XSD) string. This can lead to unauthorized access to sensitive information, including credentials from cloud metadata services, Kubernetes API, internal MinIO, and other internal network endpoints. Additionally, it enables local file reads of critical data such as service account tokens and pod secrets. |
| A flaw was found in the MaaS API. This vulnerability allows any pod within the cluster to bypass the Kuadrant AuthPolicy gateway by forging HTTP headers, specifically `X-MaaS-Username` and `X-MaaS-Group`, which are trusted verbatim. This lack of first-party authentication enables an attacker to gain unauthorized access and escalate privileges. The concrete consequences include the ability to mint Kubernetes ServiceAccount tokens in other tenants' namespaces, revoke API keys, and exfiltrate sensitive model access configuration. |
| A flaw was found in the Red Hat OpenShift AI (RHOAI) MaaS Gateway. Improper configuration of the Gateway in a model-serving context allows a standard user with low privileges to intercept, read, log, and alter all MaaS model traffic. This includes sensitive information such as access keys, input prompts, and outputs, leading to significant information disclosure and data tampering. |
| A flaw was found in the multicloud-operators-subscription component. This vulnerability allows a user on a managed cluster to escalate their privileges by creating a Subscription with specific, crafted annotations. Successful exploitation grants the attacker the ability to deploy resources into any namespace with the elevated permissions of the controller's Service Account, potentially leading to unauthorized access and control over cluster resources. |
| A flow has been identified into dnssec.c library, causing an infinite loop to dnsmasq service. An attacker who controls any DNSSEC-signed zone can hang the dnsmasq process with a single crafted response, killing all DNS resolution for its clients. |
| A flaw was found in polkit. A local user can exploit this by providing a specially crafted, excessively long input to the `polkit-agent-helper-1` setuid binary via standard input (stdin). This unbounded input can lead to an out-of-memory (OOM) condition, resulting in a Denial of Service (DoS) for the system. |
| A flaw was found in the Konnectivity proxy-server configuration for hosted control planes. The agent-facing listener was started without --cluster-ca-cert (and without token-based agent authentication), so client certificates were not validated. A remote attacker who can reach the Konnectivity cluster endpoint could connect as an unauthenticated agent, join the routing pool, and potentially proxy, inspect, modify, or drop control-plane-to-node traffic. |
| A flaw was found in the OpenShift Router. A user with EndpointSlice write access can exploit this vulnerability by creating a Service backed by an FQDN (Fully Qualified Domain Name) EndpointSlice that resolves to a cloud metadata endpoint. This allows the router to proxy requests to the cloud metadata endpoint, leading to the disclosure of instance credentials and other sensitive metadata. This bypasses previous security measures for validating IP addresses. |
