| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| A flaw was found in FFmpeg. The tdsc_load_cursor() function writes beyond
the bounds of a heap-allocated buffer when processing crafted TDSC cursor
data. A remote attacker could exploit this by supplying a specially crafted
video file, potentially leading to a denial of service or arbitrary code
execution. |
| JavaScript Cookie is a JavaScript API for handling cookies, client-side. Prior to version 3.0.7, js-cookie's internal assign() helper copies properties with for...in + plain assignment. When the source object is produced by JSON.parse, the JSON object's "__proto__" member is an own enumerable property, so the for…in enumerates it and the target[key] = source[key] write triggers the Object.prototype.__proto__ setter on the fresh target ({}). The result is a per-instance prototype hijack: Object.prototype itself is untouched, but the merged attributes object now inherits attacker-controlled keys. Because the consuming set() function then enumerates the merged object with another for...in, every key the attacker placed on the polluted prototype lands in the resulting Set-Cookie string as an attribute pair. The attacker can set domain=, secure=, samesite=, expires=, and path= on cookies whose attributes the developer thought were locked down. This issue has been patched in version 3.0.7. |
| 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 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 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 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 Hibernate. A remote attacker with low privileges could exploit a second-order SQL injection vulnerability by providing specially crafted, unsanitized non-alphanumeric characters in the ID column when the InlineIdsOrClauseBuilder is used. This could lead to sensitive information disclosure, such as reading system files, and allow for data manipulation or deletion within the application's database, resulting in an application level denial of service. |
| A flaw was found in odh-dashboard. An authenticated user of the dashboard can exploit a vulnerability related to how RoleBindings are created. The system does not properly validate the `roleRef` field, allowing a user to specify an arbitrary role, including highly privileged ones like `cluster-admin`. This can lead to privilege escalation, where an attacker gains unauthorized elevated access within their namespace and potentially persistent control over the system. |
| A flaw was found in the Feast operator. A malicious tenant could inject arbitrary code into their feature repository. This code would be executed by an automated process with elevated privileges, allowing the tenant to steal sensitive credentials. This could lead to a direct escalation of privileges, granting the tenant administrative control over the Kubernetes cluster. |
| A flaw was found in Feast. An authorization bypass vulnerability exists in the /materialize and /materialize-incremental endpoints. By sending a specially crafted request that omits the feature_views field, an attacker can bypass intended permission checks. This allows an unauthenticated remote attacker, or any authenticated user, to trigger a full re-materialization of all feature views. The consequence is a Denial of Service (DoS) due to data corruption and significant resource consumption across all tenants. |
| 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. |
| A flaw was found in odh-dashboard. This vulnerability allows an attacker, who has compromised the dashboard's Service Account (SA) token, to exploit overly broad permissions granted to the SA. This enables the attacker to escalate their privileges to cluster-administrator level, gain access to sensitive data like credentials and keys across the entire cluster, and disrupt multi-tenant isolation. |
| A flaw was found in Feast and feast-operator. The default configuration for both the Feast SDK and the feast-operator is "no_auth," meaning no security manager is installed. This default allows unauthenticated and unauthorized access to feature-server, registry-server, and offline-server endpoints. A remote attacker, by exploiting this missing authentication, could achieve remote code execution (RCE) by storing a malicious User-Defined Function (UDF) on the feature-server, trigger a denial of service (DoS) by forcing re-materialization of all tenant features, and gain unauthorized access to cross-tenant data. |
| A flaw was found in the `odh-model-controller`. An authenticated user with permissions to create custom resources can exploit a vulnerability in the `loadSecret` function. This function improperly reads the Secret namespace from user-controlled input without validation. This allows an attacker to read sensitive API keys and cloud credentials from other namespaces, leading to information disclosure. |
| A flaw was found in the TrustyAI Service Operator. When deploying services like gorch or NemoGuardrails, if a specific security setting is not enabled, these services can expose their communication channels without requiring users to prove their identity. This allows any other program within the cluster to access the AI guardrails and orchestrator without proper authorization. An attacker could exploit this to gain unauthorized access to sensitive information and potentially make limited changes to the AI models. |
| A vulnerability was identified in the Feast Feature Server's `/ws/chat` endpoint that allows remote attackers to establish persistent WebSocket connections without any authentication. By opening a large number of simultaneous connections, an attacker can exhaust server resources—such as memory, CPU, and file descriptors—leading to a complete denial of service for legitimate users. |
| A flaw was found in the vllm-orchestrator-gateway component. The system's production binary logs all incoming authorization headers and full chat payloads, which may contain personally identifiable information (PII) and secrets, to persistent logs. This sensitive data, including bearer tokens and chat content, can be accessed by any user with logging privileges. This vulnerability leads to information disclosure, potentially allowing an attacker to harvest credentials and sensitive conversation content. |
