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| CVE | CVSS | EPSS | Vendor, product | Description | Modified |
|---|---|---|---|---|---|
| CVE-2026-42271KEVexploit ×3 | 8.7 high | 91.0% | litellm, redhat, openshift_ai | LiteLLM is a proxy server (AI Gateway) to call LLM APIs in OpenAI (or native) format. From version 1.74.2 to before version 1.83.7, two endpoints used to preview an MCP server before saving it — POST /mcp-rest/test/connection and POST /mcp-rest/test/tools/list — accepted a full server configuration in the request body, including the command, args, and env fields used by the stdio transport. When called with a stdio configuration, the endpoints attempted to connect, which spawned the supplied command as a subprocess on the proxy host with the privileges of the proxy process. The endpoints were gated only by a valid proxy API key, with no role check. Any authenticated user — including holders of low-privilege internal-user keys — could therefore run arbitrary commands on the host. This issue has been patched in version 1.83.7. | Mon 26 Oct |
| CVE-2026-13717 | 8.8 high | 1.0% | no CPE yet | 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. | Mon 26 Oct |
| CVE-2026-15218 | 7.9 high | 1.0% | no CPE yet | 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. | 19 Oct |
| CVE-2026-4775 | 7.8 high | 1.0% | libtiff, debian, debian_linux, ai_inference_server | A flaw was found in the libtiff library. A remote attacker could exploit a signed integer overflow vulnerability in the putcontig8bitYCbCr44tile function by providing a specially crafted TIFF file. This flaw can lead to an out-of-bounds heap write due to incorrect memory pointer calculations, potentially causing a denial of service (application crash) or arbitrary code execution. | Mon 26 Oct |
| CVE-2026-39979 | 6.9 medium | 1.0% | jqlang, redhat, jq, ai_inference_server, ansible_automation_platform | jq is a command-line JSON processor. In commits before 2f09060afab23fe9390cce7cb860b10416e1bf5f, the jv_parse_sized() API in libjq accepts a counted buffer with an explicit length parameter, but its error-handling path formats the input buffer using %s in jv_string_fmt(), which reads until a NUL terminator is found rather than respecting the caller-supplied length. This means that when malformed JSON is passed in a non-NUL-terminated buffer, the error construction logic performs an out-of-bounds read past the end of the buffer. The vulnerability is reachable by any libjq consumer calling jv_parse_sized() with untrusted input, and depending on memory layout, can result in memory disclosure or process termination. The issue has been patched in commit 2f09060afab23fe9390cce7cb860b10416e1bf5f. | Mon 26 Oct |
| CVE-2026-5483 | 9.9 critical | 0.00% | redhat, openshift_ai | A flaw was found in odh-dashboard in Red Hat Openshift AI. This vulnerability in the `odh-dashboard` component of Red Hat OpenShift AI (RHOAI) allows for the disclosure of Kubernetes Service Account tokens through a NodeJS endpoint. This could enable an attacker to gain unauthorized access to Kubernetes resources. | Mon 26 Oct |
| CVE-2026-16745 | 8.8 high | 0.00% | no CPE yet | 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. | Sat 24 Oct |
| CVE-2026-15154 | 6.5 medium | 0.00% | redhat, openshift_ai | 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. | Sun 25 Oct |
| CVE-2026-17557 | 6.5 medium | 0.00% | no CPE yet | A flaw was found in the `lm-evaluation-harness` component of Red Hat OpenShift AI. An authenticated user, able to submit an evaluation job, could exploit this by providing a malicious model URL in the `JobSpec`. This vulnerability allows for the disclosure of the tenant's model authentication secret to an attacker-controlled external listener. Additionally, it enables Server-Side Request Forgery (SSRF) attacks against internal network endpoints reachable from the evaluation job pod. | Thu 22 Oct |
| CVE-2026-86332 | 6.5 medium | 0.00% | no CPE yet | 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). | Thu 22 Oct |
| CVE-2025-12103 | 5.0 medium | 0.00% | no CPE yet | A flaw was found in Red Hat Openshift AI Service. The TrustyAI component is granting all service accounts and users on a cluster permissions to get, list, watch any pod in any namespace on the cluster. TrustyAI is creating a role `trustyai-service-operator-lmeval-user-role` and a CRB `trustyai-service-operator-default-lmeval-user-rolebinding` which is being applied to `system:authenticated` making it so that every single user or service account can get a list of pods running in any namespace on the cluster Additionally users can access all `persistentvolumeclaims` and `lmevaljobs` | 18 Oct |
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EPSS scores: 2026-10-27. NVD cursor: 2026-10-28 22:21. The News page shows only notable CVEs (KEV, your stack, new criticals, EPSS over 50%, or active exploitation per SSVC); this table has all of them.