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Red Hat OpenShift AI 2.25 · Red Hat OpenShift AI 3.3 · Red Hat OpenShift AI 3.4 · Red Hat AI Inference Server

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.

Red Hat OpenShift AI 2.25 · Red Hat OpenShift AI 3.3 · Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI 3.5

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.

Red Hat OpenShift AI 2.25 · Red Hat OpenShift AI 3.3 · Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI 3.5

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.

Red Hat OpenShift AI 2.25 · Red Hat OpenShift AI 3.3 · Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI 3.5

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.

Red Hat OpenShift AI 2.25 · Red Hat OpenShift AI 3.3 · Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI 3.5

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.

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An out-of-bounds write vulnerability was found in the BFD library's DLX ELF backend (bfd/elf32-dlx.c) in GNU binutils. The dlx_rtype_to_howto() function maps ELF relocation types to internal howto structures but fails to perform adequate bounds checking on attacker-controlled relocation type values (via ELF32_R_TYPE(r_info)) before indexing into the dlx_elf_howto_table[] array. The DLX relocation type number space is non-contiguous (basic types 0-6, extended types at 0x10000+), but the default case in the switch statement allows arbitrary index values to reach the array access. A specially crafted ELF/DLX object file can trigger this out-of-bounds write when processed by any BFD-consuming tool (objdump, readelf, strip, ld, nm, objcopy). The vulnerability has been demonstrated to achieve arbitrary code execution via a File Stream Oriented Programming (FSOP) attack against glibc FILE structures (stderr), redirecting control flow to system(). Attack scenarios include CI/CD pipelines performing automated binary analysis, developer workstations running objdump/readelf on untrusted binaries, automated security scanning or malware analysis tools invoking binutils, and package build systems processing third-party code. Note: This vulnerability is only exploitable when binutils is built with the DLX backend enabled (typically via --enable-targets=all).

keras-team/keras · Red Hat OpenShift AI 2.25 · Red Hat OpenShift AI (RHOAI) · Keras keras

Keras versions prior to 3.14.0 are vulnerable to a path traversal issue in the archive extraction utilities located in `keras/src/utils/file_utils.py`. The functions `filter_safe_tarinfos()` and `filter_safe_zipinfos()` validate archive member paths against the process current working directory (CWD) instead of the actual extraction destination. When the process runs with CWD set to `/`, which is common in Docker containers, CI/CD runners, and Jupyter environments, the validation boundary becomes the filesystem root, allowing traversal paths to bypass the security check. Additionally, the zip filter contains a bug that causes an `AttributeError` when a blocked entry is encountered, leading to incomplete extraction. Furthermore, Python 3.11 installations lack the `filter="data"` safety net, leaving them entirely reliant on the flawed CWD-based filter. Exploitation of this vulnerability can result in arbitrary file writes outside the intended extraction directory, enabling attackers to overwrite configuration files, inject malicious code, or corrupt machine learning datasets and pipelines.