RHSA-2026:60367
Vulnerability from csaf_redhat - Published: 2026-08-26 17:09 - Updated: 2026-09-22 18:11Summary
Red Hat Security Advisory: RHOAI 3.5 - Red Hat OpenShift AI
Severity
Critical
Notes
Topic: Updated images are now available for Red Hat OpenShift AI.
Details: Release of RHOAI 3.5 provides these changes:
Terms of Use: This content is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). If you distribute this content, or a modified version of it, you must provide attribution to Red Hat Inc. and provide a link to the original.
A path traversal and arbitrary file overwrite vulnerability has been identified in Argo Workflows during the extraction of archived artifacts, where symbolic links inside a crafted archive are not safely validated before file extraction. An attacker could exploit this flaw by submitting a malicious archive containing symbolic links that point outside the intended extraction directory, causing files to be written or overwritten in unintended locations within the workflow pod. Successful exploitation may allow an attacker to overwrite execution control files and achieve arbitrary command execution during pod startup.
8.1 (High)
Affected products
Fixed
4 products
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-prediction-rhel9@sha256:5743e64bd3a2947dc022830f2d769f4ff90daf04b3e6dcfaba6420815cdb52e5_arm64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-prediction-rhel9@sha256:ada20174176992b0ee306e6eface5bff06f06a71783f3deccd530a5bc4fe80a1_amd64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-training-rhel9@sha256:4f1a03f0801811aea4550346a041c079d1cbb80cdb6f128a513122eebe3f2cef_arm64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-training-rhel9@sha256:5bdb27b5f11d3057f9087b1e08e68b13e107541d595cbf9066d4f7a96a472ae9_amd64 | — |
Vendor Fix
fix
Workaround
|
Threats
Impact
Important
A flaw was found in pip, the package installer for Python. A remote attacker can exploit this vulnerability by tricking a victim into installing a malicious Python wheel. This wheel contains specially crafted entry-point names that use directory traversal or absolute paths. This allows pip to write generated script wrappers outside the intended installation directory, leading to arbitrary file overwrite. This can severely impact system integrity and availability, and in certain scenarios, may lead to arbitrary code execution.
8.0 (High)
Affected products
Fixed
4 products, the same list as for
CVE-2025-66626
Threats
Impact
Important
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.
8.1 (High)
Affected products
Fixed
4 products, the same list as for
CVE-2025-66626
Threats
Impact
Important
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.
8.0 (High)
Affected products
Fixed
4 products, the same list as for
CVE-2025-66626
Threats
Impact
Important
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.
8.7 (High)
Affected products
Fixed
4 products, the same list as for
CVE-2025-66626
Threats
Impact
Important
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.
7.5 (High)
Affected products
Fixed
4 products, the same list as for
CVE-2025-66626
Threats
Impact
Moderate
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.
8.8 (High)
Affected products
Fixed
4 products, the same list as for
CVE-2025-66626
Threats
Impact
Important
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.
7.5 (High)
Affected products
Fixed
4 products, the same list as for
CVE-2025-66626
Threats
Impact
Moderate
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.
7.1 (High)
Affected products
Fixed
4 products, the same list as for
CVE-2025-66626
Threats
Impact
Moderate
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.
8.8 (High)
Affected products
Fixed
4 products, the same list as for
CVE-2025-66626
Threats
Impact
Critical
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.
8.8 (High)
Affected products
Fixed
4 products, the same list as for
CVE-2025-66626
Threats
Impact
Critical
A flaw was found in PyJWT, a Python library for JSON Web Token (JWT) implementation. When decoding JWTs, the library fails to validate the use of JSON Web Keys (JWK) in the HMAC algorithm while also supporting asymmetric algorithms. This allows a remote attacker to use the issuer's public key as the secret key for the HMAC algorithm, leading to the ability to forge JWTs. This vulnerability can result in authentication bypass or unauthorized access.
7.4 (High)
Affected products
Fixed
4 products, the same list as for
CVE-2025-66626
Threats
Impact
Important
A flaw was found in MLflow. An unauthenticated remote attacker can exploit a Server-Side Request Forgery (SSRF) vulnerability by sending a specially crafted request to the webhook test endpoint. This occurs because the system validates only the initial URL, but then follows unvalidated HTTP redirects, allowing the attacker to bypass security controls. Successful exploitation can lead to information disclosure, enabling access to internal or cloud metadata services and sensitive data.
8.5 (High)
Affected products
Fixed
4 products, the same list as for
CVE-2025-66626
Threats
Exploit Status
CISA: https://www.cisa.gov/known-exploited-vulnerabilities-catalog
Impact
Important
References
81 references
Acknowledgments
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"name": "External Control of File Name or Path"
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"discovery_date": "2025-12-09T21:01:10.560389+00:00",
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"category": "description",
"text": "A path traversal and arbitrary file overwrite vulnerability has been identified in Argo Workflows during the extraction of archived artifacts, where symbolic links inside a crafted archive are not safely validated before file extraction. An attacker could exploit this flaw by submitting a malicious archive containing symbolic links that point outside the intended extraction directory, causing files to be written or overwritten in unintended locations within the workflow pod. Successful exploitation may allow an attacker to overwrite execution control files and achieve arbitrary command execution during pod startup.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "github.com/argoproj/argo-workflows: argoproj/argo-workflows is vulnerable to RCE via ZipSlip and symbolic links",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "Red Hat Product Security has rated this issue as High severity (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H, 8.1) because an attacker with basic workflow submission privileges can supply a specially crafted archive that is automatically extracted without proper validation. The attack complexity is low and does not require user interaction once the malicious workflow is submitted. Successful exploitation allows arbitrary file overwrite within the affected pod, including critical execution files, which can result in code execution at pod startup. While the impact is generally limited to the compromised pod and does not directly lead to host-level compromise, the integrity and availability impacts within the container are significant, justifying a High severity rating.",
"title": "Statement"
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"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
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},
{
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"text": "This Important flaw in pip\u0027s wheel installation process allows for arbitrary file overwrite due to path traversal. An attacker could exploit this by convincing a user to install a specially crafted malicious Python wheel. While file overwrites are limited to the installing user\u0027s permissions, using `pip install` with elevated privileges in Red Hat environments significantly increases the potential impact, potentially leading to system integrity compromise or arbitrary code execution.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
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"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
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},
{
"cve": "CVE-2026-15581",
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"ids": [
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"title": "Vulnerability description"
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{
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"text": "trustyai-service-operator: trustyai-service-operator: TAS internal Service bypasses kube-rbac-proxy, exposing unauthenticated Quarkus API cluster-wide",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This vulnerability is rated as Important. The TrustyAI Service (TAS) in Red Hat OpenShift AI (RHOAI) deployments exposes its backend API without authentication to any pod on the cluster network, bypassing the intended kube-rbac-proxy security control. This allows unauthorized pods in other namespaces to access, tamper with, or delete another tenant\u0027s TAS monitoring data and configuration, leading to data integrity and availability concerns.",
"title": "Statement"
},
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"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
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{
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"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This Important vulnerability in the Data Science Pipelines Operator (DSPO) for Red Hat OpenShift AI grants the operator\u0027s ServiceAccount excessive cluster-wide permissions. These overprivileged permissions, including `pods/exec` and `clusterrolebindings` CRUD, mean that a compromise of the DSPO pod could escalate to full cluster-admin privileges. This significantly amplifies the blast radius of any initial compromise within the OpenShift AI environment.",
"title": "Statement"
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{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
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}
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{
"cve": "CVE-2026-18611",
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"title": "Vulnerability description"
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"title": "Vulnerability summary"
},
{
"category": "other",
"text": "The Data Science Pipelines Operator in Red Hat OpenShift AI is susceptible to a Moderate severity vulnerability where it generates weak credentials for MariaDB and MinIO using a cryptographically insecure pseudo-random number generator if users do not provide their own. An unauthenticated attacker able to access the MinIO Route or MariaDB Service could potentially derive these credentials, leading to unauthorized access to pipeline artifacts and metadata. This risk is elevated in deployments where MinIO is exposed via a public OpenShift Route or MariaDB allows empty root passwords.",
"title": "Statement"
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"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
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},
{
"cve": "CVE-2026-18617",
"cwe": {
"id": "CWE-915",
"name": "Improperly Controlled Modification of Dynamically-Determined Object Attributes"
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"discovery_date": "2026-08-03T07:31:45.683000+00:00",
"ids": [
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"title": "Vulnerability description"
},
{
"category": "summary",
"text": "data-science-pipelines-operator: DSPO: MySQL DSN parameter injection via CustomExtraParams enables LOCAL INFILE file exfiltration from operator pod",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This Important vulnerability in Red Hat OpenShift AI allows a namespace editor to escalate privileges to cluster-admin. By injecting malicious parameters into the `spec.database.customExtraParams` field of a Data Science Pipeline Application (DSPA) Custom Resource, an attacker can force the operator pod to exfiltrate sensitive files, including its service account token, to an attacker-controlled MySQL server. This bypasses typical namespace boundaries, granting cluster-wide administrative access.",
"title": "Statement"
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{
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"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
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},
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"title": "Vulnerability summary"
},
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"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
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}
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"title": "ml-metdata: Bundled gRPC 1.46.3 (2022) with published HTTP/2 DoS CVEs \u2014 directly reachable on listener"
},
{
"cve": "CVE-2026-18620",
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"discovery_date": "2026-08-03T07:52:01.604000+00:00",
"ids": [
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"text": "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\u0027 pods.",
"title": "Vulnerability description"
},
{
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"text": "data-sciences-pipeline: User-controlled ServiceAccount for workflow pods without authorization check \u2014 confused deputy",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This Moderate severity flaw in Red Hat OpenShift AI allows a restricted tenant to execute workflow pods with elevated privileges. By manipulating the `service_account` field in a `CreateRun` request, an attacker can bypass authorization checks and utilize more privileged ServiceAccounts, potentially leading to unauthorized access to secrets and execution within co-tenant pods. This risk arises from the API server\u0027s failure to perform a SubjectAccessReview for the requested ServiceAccount.",
"title": "Statement"
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{
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"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
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"title": "data-sciences-pipeline: User-controlled ServiceAccount for workflow pods without authorization check \u2014 confused deputy"
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{
"cve": "CVE-2026-18951",
"discovery_date": "2026-08-04T19:04:31.363000+00:00",
"ids": [
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"text": "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.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "odh-training-operator-rhel9: [Trainer v2 Security] TRN-02: RHOAI overlay aggregates trainjobs CRUD into standard edit ClusterRole",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This Critical vulnerability in Red Hat OpenShift AI (RHOAI) arises from the RHOAI overlay aggregating `trainjobs` CRUD operations into the native Kubernetes `edit` ClusterRole. This implicit permission grant allows any namespace editor to create, modify, and delete `TrainJobs`, significantly widening the attack surface for privilege escalation when combined with other vulnerabilities like PodSpec passthrough. This issue is specific to the RHOAI fork and not present in upstream Kubeflow trainer.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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]
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"category": "self",
"summary": "Canonical URL",
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},
{
"category": "external",
"summary": "RHBZ#2511187",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2511187"
},
{
"category": "external",
"summary": "https://www.cve.org/CVERecord?id=CVE-2026-18951",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-18951"
},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-18951",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-18951"
}
],
"release_date": "2026-08-10T18:54:28.946000+00:00",
"remediations": [
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"category": "vendor_fix",
"date": "2026-08-26T17:09:43+00:00",
"details": "For Red Hat OpenShift AI 3.5 see the following documentation, which will be updated shortly for this release, for important instructions on how to upgrade your cluster and fully apply this errata update:\n\nhttps://docs.redhat.com/en/documentation/red_hat_openshift_ai/",
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],
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"category": "none"
},
"url": "https://access.redhat.com/errata/RHSA-2026:60367"
},
{
"category": "workaround",
"details": "Administrators should review and adjust their Kubernetes RBAC configurations within Red Hat OpenShift AI to ensure that `trainjobs` permissions are explicitly managed. This involves removing `trainjobs` from the `aggregate-to-edit` ClusterRole labels or requiring explicit `RoleBinding` for `trainjobs` access. This prevents implicit permission grants to namespace editors and reduces the attack surface. Consult Kubernetes documentation for specific instructions on modifying ClusterRoles and RoleBindings. A restart or reload of affected components may be required for changes to take effect.",
"product_ids": [
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]
}
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{
"cvss_v3": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 8.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
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]
}
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"details": "Critical"
}
],
"title": "odh-training-operator-rhel9: [Trainer v2 Security] TRN-02: RHOAI overlay aggregates trainjobs CRUD into standard edit ClusterRole"
},
{
"cve": "CVE-2026-18982",
"cwe": {
"id": "CWE-250",
"name": "Execution with Unnecessary Privileges"
},
"discovery_date": "2026-08-05T16:49:49.872000+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2511648"
}
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{
"category": "description",
"text": "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.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "odh-training-operator-rhel9: RHOAI fork aggregates training job create onto native edit/admin ClusterRoles",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This is a Critical vulnerability. The Red Hat OpenShift AI (RHOAI) fork of the training-operator aggregates training job creation permissions onto standard Kubernetes `edit` and `admin` ClusterRoles. This allows any namespace editor to escalate privileges by creating training jobs that can impersonate ServiceAccounts, mount hostPath volumes, set privileged security contexts, and achieve remote code execution, expanding the attack surface beyond users explicitly granted training workload access.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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"category": "external",
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"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2511648"
},
{
"category": "external",
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"url": "https://www.cve.org/CVERecord?id=CVE-2026-18982"
},
{
"category": "external",
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-18982"
}
],
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"date": "2026-08-26T17:09:43+00:00",
"details": "For Red Hat OpenShift AI 3.5 see the following documentation, which will be updated shortly for this release, for important instructions on how to upgrade your cluster and fully apply this errata update:\n\nhttps://docs.redhat.com/en/documentation/red_hat_openshift_ai/",
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},
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]
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"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
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"version": "3.1"
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"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-training-rhel9@sha256:5bdb27b5f11d3057f9087b1e08e68b13e107541d595cbf9066d4f7a96a472ae9_amd64"
]
}
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"threats": [
{
"category": "impact",
"details": "Critical"
}
],
"title": "odh-training-operator-rhel9: RHOAI fork aggregates training job create onto native edit/admin ClusterRoles"
},
{
"cve": "CVE-2026-48526",
"cwe": {
"id": "CWE-347",
"name": "Improper Verification of Cryptographic Signature"
},
"discovery_date": "2026-05-28T16:01:22.805235+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2482734"
}
],
"notes": [
{
"category": "description",
"text": "A flaw was found in PyJWT, a Python library for JSON Web Token (JWT) implementation. When decoding JWTs, the library fails to validate the use of JSON Web Keys (JWK) in the HMAC algorithm while also supporting asymmetric algorithms. This allows a remote attacker to use the issuer\u0027s public key as the secret key for the HMAC algorithm, leading to the ability to forge JWTs. This vulnerability can result in authentication bypass or unauthorized access.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "python-pyjwt: PyJWT: Authentication bypass due to forged JSON Web Tokens",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This Important vulnerability in PyJWT allows for authentication bypass and unauthorized access. It occurs when a JWT verifier is misconfigured to accept both symmetric and asymmetric algorithms, and a public JSON Web Key is erroneously used as the HMAC secret. Red Hat products are only affected if they utilize this specific, non-standard verifier configuration.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
"product_status": {
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"summary": "Canonical URL",
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},
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"summary": "RHBZ#2482734",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2482734"
},
{
"category": "external",
"summary": "https://www.cve.org/CVERecord?id=CVE-2026-48526",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-48526"
},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-48526",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-48526"
},
{
"category": "external",
"summary": "https://github.com/jpadilla/pyjwt/security/advisories/GHSA-xgmm-8j9v-c9wx",
"url": "https://github.com/jpadilla/pyjwt/security/advisories/GHSA-xgmm-8j9v-c9wx"
}
],
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"date": "2026-08-26T17:09:43+00:00",
"details": "For Red Hat OpenShift AI 3.5 see the following documentation, which will be updated shortly for this release, for important instructions on how to upgrade your cluster and fully apply this errata update:\n\nhttps://docs.redhat.com/en/documentation/red_hat_openshift_ai/",
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}
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"scores": [
{
"cvss_v3": {
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 7.4,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:N",
"version": "3.1"
},
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]
}
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"threats": [
{
"category": "impact",
"details": "Important"
}
],
"title": "python-pyjwt: PyJWT: Authentication bypass due to forged JSON Web Tokens"
},
{
"cve": "CVE-2026-64849",
"cwe": {
"id": "CWE-918",
"name": "Server-Side Request Forgery (SSRF)"
},
"discovery_date": "2026-08-17T21:31:34.221062+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2517655"
}
],
"notes": [
{
"category": "description",
"text": "A flaw was found in MLflow. An unauthenticated remote attacker can exploit a Server-Side Request Forgery (SSRF) vulnerability by sending a specially crafted request to the webhook test endpoint. This occurs because the system validates only the initial URL, but then follows unvalidated HTTP redirects, allowing the attacker to bypass security controls. Successful exploitation can lead to information disclosure, enabling access to internal or cloud metadata services and sensitive data.",
"title": "Vulnerability description"
},
{
"category": "summary",
"text": "mlflow: MLflow: Unauthenticated full-read SSRF in webhook delivery: _validate_webhook_url bypassed via unvalidated HTTP redirects (and DNS rebinding)",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "This flaw is in MLflow Tracking Server webhook delivery. An attacker who can reach the MLflow API can abuse POST /api/2.0/mlflow/webhooks/{id}/test to trigger SSRF and read upstream response bodies. Upstream rates this against the default unauthenticated mlflow server (PR:N).\n\nFor OpenShift AI, the operator-managed odh-mlflow-rhel9 image is the primary exposure; the operator enables kubernetes-auth by default, so unauthenticated abuse applies only where MLflow is deployed without authentication. The other 19 RHOAI images embed the mlflow Python package as a client library and do not execute the vulnerable server webhook path in their default role concluding those 19 RHOAI images as Not Affected.\n\nImpact is set to Important with PR:L reflecting typical authenticated RHOAI deployments.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
"product_status": {
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"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-prediction-rhel9@sha256:ada20174176992b0ee306e6eface5bff06f06a71783f3deccd530a5bc4fe80a1_amd64",
"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-training-rhel9@sha256:4f1a03f0801811aea4550346a041c079d1cbb80cdb6f128a513122eebe3f2cef_arm64",
"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-training-rhel9@sha256:5bdb27b5f11d3057f9087b1e08e68b13e107541d595cbf9066d4f7a96a472ae9_amd64"
]
},
"references": [
{
"category": "self",
"summary": "Canonical URL",
"url": "https://access.redhat.com/security/cve/CVE-2026-64849"
},
{
"category": "external",
"summary": "RHBZ#2517655",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=2517655"
},
{
"category": "external",
"summary": "https://www.cve.org/CVERecord?id=CVE-2026-64849",
"url": "https://www.cve.org/CVERecord?id=CVE-2026-64849"
},
{
"category": "external",
"summary": "https://nvd.nist.gov/vuln/detail/CVE-2026-64849",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-64849"
},
{
"category": "external",
"summary": "https://github.com/mlflow/mlflow/commit/ba949522477cbd5915aa55d29b0cfad7d5ddf939",
"url": "https://github.com/mlflow/mlflow/commit/ba949522477cbd5915aa55d29b0cfad7d5ddf939"
},
{
"category": "external",
"summary": "https://github.com/mlflow/mlflow/issues/24179",
"url": "https://github.com/mlflow/mlflow/issues/24179"
},
{
"category": "external",
"summary": "https://github.com/mlflow/mlflow/pull/24258",
"url": "https://github.com/mlflow/mlflow/pull/24258"
},
{
"category": "external",
"summary": "https://github.com/mlflow/mlflow/releases/tag/v3.15.0",
"url": "https://github.com/mlflow/mlflow/releases/tag/v3.15.0"
},
{
"category": "external",
"summary": "https://github.com/mlflow/mlflow/security/advisories/GHSA-7gwp-5pfp-969j",
"url": "https://github.com/mlflow/mlflow/security/advisories/GHSA-7gwp-5pfp-969j"
},
{
"category": "external",
"summary": "https://www.cisa.gov/known-exploited-vulnerabilities-catalog",
"url": "https://www.cisa.gov/known-exploited-vulnerabilities-catalog"
}
],
"release_date": "2026-08-17T21:16:10.612000+00:00",
"remediations": [
{
"category": "vendor_fix",
"date": "2026-08-26T17:09:43+00:00",
"details": "For Red Hat OpenShift AI 3.5 see the following documentation, which will be updated shortly for this release, for important instructions on how to upgrade your cluster and fully apply this errata update:\n\nhttps://docs.redhat.com/en/documentation/red_hat_openshift_ai/",
"product_ids": [
"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-prediction-rhel9@sha256:5743e64bd3a2947dc022830f2d769f4ff90daf04b3e6dcfaba6420815cdb52e5_arm64",
"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-prediction-rhel9@sha256:ada20174176992b0ee306e6eface5bff06f06a71783f3deccd530a5bc4fe80a1_amd64",
"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-training-rhel9@sha256:4f1a03f0801811aea4550346a041c079d1cbb80cdb6f128a513122eebe3f2cef_arm64",
"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-training-rhel9@sha256:5bdb27b5f11d3057f9087b1e08e68b13e107541d595cbf9066d4f7a96a472ae9_amd64"
],
"restart_required": {
"category": "none"
},
"url": "https://access.redhat.com/errata/RHSA-2026:60367"
},
{
"category": "workaround",
"details": "To reduce the attack surface for this vulnerability, restrict network access to the MLflow server. Implement firewall rules or network access controls to limit connectivity to the MLflow instance from untrusted networks. This operational control helps prevent unauthenticated attackers from reaching the vulnerable webhook test endpoint.",
"product_ids": [
"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-prediction-rhel9@sha256:5743e64bd3a2947dc022830f2d769f4ff90daf04b3e6dcfaba6420815cdb52e5_arm64",
"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-prediction-rhel9@sha256:ada20174176992b0ee306e6eface5bff06f06a71783f3deccd530a5bc4fe80a1_amd64",
"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-training-rhel9@sha256:4f1a03f0801811aea4550346a041c079d1cbb80cdb6f128a513122eebe3f2cef_arm64",
"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-training-rhel9@sha256:5bdb27b5f11d3057f9087b1e08e68b13e107541d595cbf9066d4f7a96a472ae9_amd64"
]
}
],
"scores": [
{
"cvss_v3": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 8.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "LOW",
"privilegesRequired": "LOW",
"scope": "CHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:N",
"version": "3.1"
},
"products": [
"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-prediction-rhel9@sha256:5743e64bd3a2947dc022830f2d769f4ff90daf04b3e6dcfaba6420815cdb52e5_arm64",
"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-prediction-rhel9@sha256:ada20174176992b0ee306e6eface5bff06f06a71783f3deccd530a5bc4fe80a1_amd64",
"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-training-rhel9@sha256:4f1a03f0801811aea4550346a041c079d1cbb80cdb6f128a513122eebe3f2cef_arm64",
"Red Hat OpenShift AI 3.5:registry.redhat.io/rhoai/odh-latency-predictor-training-rhel9@sha256:5bdb27b5f11d3057f9087b1e08e68b13e107541d595cbf9066d4f7a96a472ae9_amd64"
]
}
],
"threats": [
{
"category": "exploit_status",
"date": "2026-08-19T00:00:00+00:00",
"details": "CISA: https://www.cisa.gov/known-exploited-vulnerabilities-catalog"
},
{
"category": "impact",
"details": "Important"
}
],
"title": "mlflow: MLflow: Unauthenticated full-read SSRF in webhook delivery: _validate_webhook_url bypassed via unvalidated HTTP redirects (and DNS rebinding)"
}
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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