Export limit exceeded: 389762 CVEs match your query. Please refine your search to export 10,000 CVEs or fewer.
Search
Search Results (83 CVEs found)
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2026-18620 | 1 Redhat | 2 Openshift Ai, Openshift Ai 3.3 | 2026-09-08 | 7.1 High |
| A flaw was found in Data Science Pipelines. A restricted user, or tenant, can exploit an improper authorization vulnerability in the setDefaultServiceAccount function. By specifying a more privileged ServiceAccount (SA) during a CreateRun request, an attacker can bypass authorization checks. This allows the tenant to run their containers with elevated privileges, potentially leading to the disclosure of sensitive information (secrets) and the ability to execute commands within other users' pods. | ||||
| CVE-2026-18611 | 2 Red Hat, Redhat | 2 Red Hat Openshift Ai (rhoai), Openshift Ai | 2026-09-08 | 7.5 High |
| 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. | ||||
| CVE-2026-15154 | 1 Redhat | 1 Openshift Ai | 2026-09-08 | 6.5 Medium |
| 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. | ||||
| CVE-2026-18621 | 2 Red Hat, Redhat | 3 Red Hat Openshift Ai (rhoai), Ai Inference Server, Openshift Ai | 2026-09-08 | 7.6 High |
| 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. | ||||
| CVE-2026-18617 | 1 Redhat | 1 Openshift Ai | 2026-09-08 | 8.8 High |
| 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. | ||||
| CVE-2026-18608 | 2 Red Hat, Redhat | 2 Red Hat Openshift Ai (rhoai), Openshift Ai | 2026-09-08 | 8.7 High |
| 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. | ||||
| CVE-2026-16745 | 2 Red Hat, Redhat | 2 Red Hat Openshift Ai (rhoai), Openshift Ai | 2026-09-08 | 8.8 High |
| 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. | ||||
| CVE-2026-15581 | 2 Red Hat, Redhat | 2 Red Hat Openshift Ai (rhoai), Openshift Ai | 2026-09-08 | 8 High |
| 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. | ||||
| CVE-2026-15378 | 2 Red Hat, Redhat | 2 Red Hat Openshift Ai (rhoai), Openshift Ai | 2026-09-08 | 9.3 Critical |
| 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. | ||||
| CVE-2026-86332 | 2 Red Hat, Redhat | 2 Red Hat Openshift Ai (rhoai), Openshift Ai | 2026-09-08 | 6.5 Medium |
| A flaw was found in odh-dashboard in Red Hat OpenShift AI. The backend-for-frontend route GET /api/nim-serving/:nimResource reads Kubernetes Secrets using the dashboard service account and returns the full Secret object, including .data, without an authorization check. Any authenticated dashboard user can retrieve the cluster NVIDIA NGC API key Secret (apiKeySecret) and the NIM image pull secret (nimPullSecret). Create and delete of the same NIM credential are admin-gated; the read path is not. This is missing authorization (CWE-862) and insufficiently protected credentials (CWE-522). It is distinct from CVE-2026-5483 (service-account token leak in the Kubernetes client response wrapper on the same route) and CVE-2026-16456 (odh-model-controller cross-namespace confused deputy). | ||||
| CVE-2026-84185 | 2 Red Hat, Redhat | 6 Red Hat Openshift Ai (rhoai), Ansible Automation Platform, Enterprise Linux and 3 more | 2026-09-04 | 5.9 Medium |
| A flaw was found in the jwcrypto library, which is used for implementing Javascript Object Signing and Encryption (JOSE) standards. The issue occurs when the library verifies a General JSON Serialization JWS using a set of keys. Due to a coding error, the library fails to correctly identify the specific key ID (kid) and may instead accept a signature made by any valid key in the set. This can allow an attacker with a valid key to bypass authorization checks in applications that rely on the key ID to identify specific tenants or users. | ||||
| CVE-2026-48710 | 3 Encode, Kludex, Redhat | 9 Starlette, Starlette, Ai Inference Server and 6 more | 2026-09-04 | 6.5 Medium |
| Starlette is a lightweight ASGI framework/toolkit. Prior to version 1.0.1, the HTTP `Host` request header was not validated before being used to reconstruct `request.url`. Because the routing algorithm relies on the raw HTTP path while `request.url` is rebuilt from the `Host` header, a malformed header could make `request.url.path` differ from the path that was actually requested. Middleware and endpoints that apply security restrictions based on `request.url` (rather than the raw `scope` path) could therefore be bypassed. Users should upgrade to a version greater than or equal to version 1.0.1, which validates the `Host` header against the grammar of RFC 9112 §3.2 / RFC 3986 §3.2.2 when constructing `request.url` and falls back to `scope["server"]` for malformed values. | ||||
| CVE-2026-34993 | 3 Aio-libs, Aiohttp, Redhat | 3 Aiohttp, Aiohttp, Openshift Ai | 2026-09-04 | 6.4 Medium |
| AIOHTTP is an asynchronous HTTP client/server framework for asyncio and Python. Prior to version 3.14.0, using ``CookieJar.load()`` with untrusted input may allow arbitrary code execution. Most applications using this function will be doing so with the user's own data, so this is unlikely to affect many applications. Version 3.14.0 patches the issue. If an application does allow attacker controlled files to be loaded, a workaround on older releases would be to sanitize the files before loading. | ||||
| CVE-2024-11831 | 1 Redhat | 34 Acm, Advanced Cluster Security, Ansible Automation Platform and 31 more | 2026-09-01 | 5.4 Medium |
| A flaw was found in npm-serialize-javascript. The vulnerability occurs because the serialize-javascript module does not properly sanitize certain inputs, such as regex or other JavaScript object types, allowing an attacker to inject malicious code. This code could be executed when deserialized by a web browser, causing Cross-site scripting (XSS) attacks. This issue is critical in environments where serialized data is sent to web clients, potentially compromising the security of the website or web application using this package. | ||||
| CVE-2026-80179 | 2 Red Hat, Redhat | 6 Red Hat Openshift Ai (rhoai), Ansible Automation Platform, Enterprise Linux and 3 more | 2026-09-01 | 5.9 Medium |
| A flaw was found in jwcrypto. A remote attacker can send a specially crafted JSON Web Encryption (JWE) token containing numerous period delimiters. This malformed token can force the JWE.deserialize() function to allocate excessive memory, leading to a MemoryError. This issue results in a denial of service (DoS) for services that process untrusted JWE values. | ||||
| CVE-2024-10963 | 1 Redhat | 4 Enterprise Linux, Openshift, Openshift Ai and 1 more | 2026-08-31 | 7.4 High |
| A flaw was found in pam_access, where certain rules in its configuration file are mistakenly treated as hostnames. This vulnerability allows attackers to trick the system by pretending to be a trusted hostname, gaining unauthorized access. This issue poses a risk for systems that rely on this feature to control who can access certain services or terminals. | ||||
| CVE-2026-56211 | 2 Aomedia, Redhat | 7 Libaom, Ai Inference Server, Enterprise Linux and 4 more | 2026-08-31 | 7.1 High |
| A remote code execution vulnerability was found in libaom, the reference AV1 codec implementation. Insufficient bounds validation in the AV1 encoder's SVC (Scalable Video Coding) layer ID control allows an attacker to supply crafted video frame pixels that overlap with internal encoder layer context structures. In fork-based video processing services, an attacker can use this to hijack the cyclic refresh map pointer, brute-force the process base address via a crash oracle, and redirect control flow to achieve arbitrary command execution. Exploitation requires the target service to use libaom with SVC encoding enabled and accept attacker-supplied video frames. | ||||
| CVE-2026-56210 | 2 Aomedia, Redhat | 7 Libaom, Ai Inference Server, Enterprise Linux and 4 more | 2026-08-31 | 7.1 High |
| A heap-buffer-overflow read vulnerability was found in libaom, the reference AV1 codec implementation. A missing bounds check in the SVC (Scalable Video Coding) layer ID control function allows setting a spatial_layer_id exceeding the configured number of layers. This causes an out-of-bounds heap read of approximately 40,728 bytes when computing a layer context array index. An attacker who can influence SVC encoder parameters in a network-facing service could exploit this for information disclosure (heap content leak) or denial of service (segmentation fault from hitting unmapped memory). | ||||
| CVE-2026-56209 | 2 Aomedia, Redhat | 7 Libaom, Ai Inference Server, Enterprise Linux and 4 more | 2026-08-31 | 7.1 High |
| An arbitrary address write vulnerability was found in libaom, the reference AV1 codec implementation. A missing bounds check in the SVC (Scalable Video Coding) layer ID control function allows an attacker to inject an arbitrary pointer into the cyclic refresh map field via crafted image pixel values. The encoder then writes approximately 1,200 bytes at the attacker-controlled address. This is fully deterministic and does not require a separate information leak. An attacker who can supply frames to a network-facing libaom encoder with SVC enabled could exploit this for denial of service or potential code execution. | ||||
| CVE-2026-56208 | 2 Aomedia, Redhat | 14 Libaom, Ai Inference Server, Enterprise Linux and 11 more | 2026-08-31 | 7.6 High |
| A heap buffer overflow vulnerability was found in libaom, the reference AV1 codec implementation. A flaw in the AV1 encoder's Look-Ahead Processing (LAP) mode causes the first-pass stats ring buffer wrap-around guard to be bypassed when g_lag_in_frames is set to 1 or higher. This results in a 232-byte out-of-bounds write on every encoded frame after the second, corrupting adjacent heap objects. An attacker who can influence encoder configuration in a transcoding service or WebRTC session could exploit this to cause a denial of service (process crash) or potentially achieve code execution. | ||||