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Red Hat Connectivity Link 1.4 · Red Hat Connectivity Link 1.5 · Red Hat Connectivity Link 1 · Red Hat OpenShift AI (RHOAI)

A flaw was found in the internal WebAssembly (WASM) shim used by the Kuadrant operator. When generating the CEL predicate that determines whether Gateway-level authentication and rate limiting should run, the shim does not correctly escape values from HTTPRoute match fields. A user who can create or modify an HTTPRoute attached to a shared Gateway can provide a crafted path, header, or query match value that causes the predicate to skip those checks. The request is still forwarded to the route’s backend.

Red Hat build of Quarkus 3.27.5.SP2 · Red Hat build of Quarkus 3.33.3.SP2 · Red Hat Exploit Intelligence · Red Hat OpenShift Serverless

A flaw was found in Quarkus HTTP security. An unauthenticated attacker can exploit a discrepancy in how paths are normalized between the security matcher and HTTP request dispatchers. This allows the attacker to craft a URL that the security matcher considers public, but which is then routed to a protected endpoint, leading to an authorization bypass and potential unauthorized access to sensitive information.

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

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.

Red Hat OpenShift AI 2.25 · Red Hat OpenShift AI 3.3 · Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI (RHOAI)

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.

Red Hat OpenShift AI 2.25 · Red Hat OpenShift AI 3.3 · Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI (RHOAI)

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.

Red Hat OpenShift AI 2.25 · Red Hat OpenShift AI 3.3 · Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI (RHOAI)

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.

Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI (RHOAI)

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.

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

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.

Feast Feature Server · Red Hat OpenShift AI (RHOAI)

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.

FFmpeg · Red Hat Enterprise Linux AI 3.0 for RHEL 9 · Red Hat Enterprise Linux AI 3.2 for RHEL 9 · Red Hat Enterprise Linux AI 3.3 for RHEL 9

FFmpeg's RASC video decoder (decode_dlta in libavcodec/rasc.c) performs 32-bit reads and writes at the row cursor before the NEXT_LINE row-boundary check and validates the DLTA region in pixel rather than byte units, so a DLTA run on a PAL8 frame can access several bytes past the row allocation. A crafted media stream using the RASC FourCC, decoded by libavcodec, triggers a bitstream-controlled out-of-bounds heap write and adjacent out-of-bounds read, leading to memory corruption.

lepture mistune · Red Hat OpenShift AI 3.4 · Red Hat Migration Toolkit for Applications 8 · Red Hat OpenShift AI (RHOAI)

Mistune is a Python Markdown parser with renderers and plugins. Prior to 3.3.0, Mistune is vulnerable to a CPU exhaustion DoS due to superlinear (approximately O(n²)) behavior in parse_link_text. When parsing Markdown containing many consecutive [ characters, parse_link_text repeatedly scans the input using a regex search inside a loop. Each iteration re-scans a large portion of the remaining string, resulting in quadratic-time behavior. An attacker-controlled Markdown input can therefore trigger excessive CPU usage with a very small payload. This vulnerability is fixed in 3.3.0.

docling-project docling · Red Hat OpenShift AI (RHOAI) · Docling docling

Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. FIn versions >= 2.82.0, < 2.91.0, if the HTML backend was explicitly configured for rendering (rendering option by default deactivated), then the Playwright-based rendering feature could allow JavaScript execution and unrestricted network access when processing untrusted HTML documents. An attacker could craft malicious HTML that executes arbitrary JavaScript in the rendering context or makes unauthorized network requests to internal services, potentially leading to SSRF attacks, data exfiltration, or remote code execution in the rendering environment. This vulnerability is fixed in 2.91.0.

Traefik · Red Hat OpenShift AI (RHOAI) · Red Hat OpenShift Dev Spaces · Traefik traefik

Traefik before 2.10.5 and 3.0.0-beta4 is affected by a denial-of-service vulnerability in HTTP/2 request handling inherited from the Go standard library's HTTP/2 implementation (CVE-2023-44487 / CVE-2023-39325, the 'Rapid Reset' technique). A remote attacker can rapidly create and cancel HTTP/2 streams to exhaust server resources and cause service unavailability.

vllm-project vllm · Red Hat AI Inference Server 3.2 · Red Hat AI Inference Server 3.3 · Red Hat AI Inference Server 3.4

vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, an assert-based security check in vLLM's activation function loading allows any unauthenticated attacker to achieve arbitrary code execution on the server by publishing a malicious HuggingFace model, when vLLM runs in Python optimized mode (python -O or PYTHONOPTIMIZE=1). This vulnerability is fixed in 0.22.0.

nltk · Red Hat OpenShift AI 2.25 · Red Hat OpenShift AI 3.4 · Red Hat Exploit Intelligence

NLTK (Natural Language Toolkit) is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. Prior to 3.10.0-rc1, nltk.data.load() in NLTK is vulnerable to path traversal via URL-encoded path separators and traversal segments when using the nltk: URL scheme. The unsafe-path regex check is performed before url2pathname() decodes the %xx sequences (a classic decode-after-check / TOCTOU-style flaw), allowing an attacker to bypass the protection documented in NLTK's SECURITY.md and read arbitrary files from the filesystem. While literal traversal strings such as ../../../etc/passwd are correctly blocked, encoded variants such as %2fetc%2fpasswd, %2e%2e%2f..., and ..%2f..%2f slip past the regex and are subsequently decoded into a real filesystem path. This vulnerability is fixed in 3.10.0-rc1.

vLLM · Red Hat AI Inference Server · Red Hat Enterprise Linux AI (RHEL AI) 3 · Red Hat OpenShift AI (RHOAI)

vLLM versions >= 0.10.2 and < 0.13.0 are missing sparse tensor validation in multimodal embeddings processing. Because PyTorch disables sparse tensor invariant checks by default, an attacker can submit crafted embedding requests with malformed (negative or out-of-bounds) tensor indices, when the prompt-embeds feature is enabled, to trigger crashes or resource exhaustion (denial of service), with potential for out-of-bounds/write-what-where memory corruption. This continues CVE-2025-62164, whose prior fix only disabled the feature by default rather than addressing the root cause.

quarkusio quarkus · Red Hat Cryostat 4 on RHEL 9 · Red Hat Build of Apache Camel 3.33 for Quarkus 3.33.2.SP1 · Red Hat build of Quarkus 3.20.6.SP2

Quarkus is a Java framework for building cloud-native applications. Prior to versions 3.37.0, 3.36.3, 3.33.2.1, 3.33.3, 3.27.4.1, 3.27.5, and 3.20.6.2, Quarkus HTTP path-based authorization policies can be bypassed using encoded semicolons (%3B) to smuggle matrix parameters past the security layer, and using encoded slashes (%2F) or backslashes (%5C) to access protected static resources. This is a distinct issue from CVE-2026-39852, which addressed only literal semicolon stripping. Versions 3.37.0, 3.36.3, 3.33.2.1, 3.33.3, 3.27.4.1, 3.27.5, and 3.20.6.2 contain a patch.

Red Hat Enterprise Linux AI 3.3 for RHEL 9 · Red Hat Enterprise Linux AI 3.4 for RHEL 9 · Red Hat Enterprise Linux AI 3.5 for RHEL 9 · Red Hat AI Inference Server 3.2

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.

Red Hat Enterprise Linux AI 3.3 for RHEL 9 · Red Hat Enterprise Linux AI 3.4 for RHEL 9 · Red Hat Enterprise Linux AI 3.5 for RHEL 9 · Red Hat AI Inference Server 3.2

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).

Red Hat Enterprise Linux AI 3.3 for RHEL 9 · Red Hat Enterprise Linux AI 3.4 for RHEL 9 · Red Hat Enterprise Linux AI 3.5 for RHEL 9 · Red Hat AI Inference Server 3.2

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.

FFmpeg · Red Hat Enterprise Linux AI 3.5 for RHEL 9 · Red Hat AI Inference Server · Red Hat Enterprise Linux AI (RHEL AI) 3

An out-of-bounds write vulnerability in FFmpeg's libavcodec library, specifically in the MagicYUV decoder, allows denial-of-service and, in some cases, can be exploited for remote code execution. This vulnerability is associated with the file libavcodec/magicyuv.C. This issue affects FFmpeg before version 8.1.2.

Kludex starlette · Red Hat AI Inference Server 3.3 · Red Hat Exploit Intelligence · Red Hat Migration Toolkit for Applications 8

Starlette is a lightweight ASGI framework/toolkit. In versions 1.0.1 and earlier, StaticFiles on Windows is vulnerable to SSRF. An UNC path such as \\attacker.com\share can cause os.path.realpath to initiate an outbound SMB connection before the path is rejected, exposing the service account’s NTLMv2 credentials for offline cracking or relay even though the HTTP response is only a 404. The issue affects default follow_symlink=False deployments, including frameworks built on Starlette such as FastAPI; POSIX systems and follow_symlink=True are unaffected. The issue is fixed in 1.1.0.

undici · Red Hat Cryostat 4 on RHEL 9 · Red Hat Enterprise Linux 10 · Red Hat Enterprise Linux 8

Impact: undici's ProxyAgent silently drops the requestTls option when configured with a SOCKS5 proxy URI (socks5:// or socks://). The target HTTPS connection through the SOCKS5 tunnel falls back to Node's default trust store, ignoring user-configured ca, cert, key, rejectUnauthorized, and servername settings. Applications that pin to an internal or corporate CA via requestTls.ca will, when their proxy URI is SOCKS5, get the default Mozilla CA bundle as the trust anchor instead. Any cert signed by any publicly-trusted CA for the target hostname is accepted, breaking the intended pin and enabling MITM read and tamper of the HTTPS exchange. Affected applications are those that use undici's ProxyAgent (or Socks5ProxyAgent directly) with SOCKS5 AND rely on requestTls for TLS scope restriction. The bug was introduced in undici 7.23.0 when SOCKS5 support was added. Patches: Upgrade to undici v7.28.0 or v8.5.0. Workarounds: No workaround is available within the SOCKS5 path. If a SOCKS5 proxy with TLS scope restriction is required and an upgrade is not yet possible, route the traffic through an HTTP-proxy ProxyAgent instead, where requestTls is honored correctly.

undici · Red Hat Cryostat 4 on RHEL 9 · Red Hat Enterprise Linux 10 · Red Hat Enterprise Linux 8

Impact: When using Socks5ProxyAgent, undici reuses a single connection pool across different origins without verifying that the pool's origin matches the requested origin. All requests are dispatched through the pool connected to the first origin, regardless of the intended destination. This causes cross-origin request routing: credentials and request data intended for origin B are sent to origin A, responses from the wrong origin are trusted, and HTTPS requests may be silently downgraded to HTTP. Impacted users are applications that use Socks5ProxyAgent (directly or via setGlobalDispatcher) and make requests to more than one origin. This was introduced in undici 7.23.0 via PR #4385 and affects all versions through 8.1.0. Patches: Upgrade to undici v7.26.0 or v8.2.0. Workarounds: Use a separate Socks5ProxyAgent instance per origin, or avoid using Socks5ProxyAgent with multiple origins.

netty · Red Hat Cryostat 4 on RHEL 9 · Red Hat AMQ Broker 7.14.1 · Red Hat Build of Apache Camel 3.33 for Quarkus 3.33.2.SP1

Netty is a network application framework for development of protocol servers and clients. Prior to versions 4.1.135.Final and 4.2.15.Final, the HAProxy PROXY protocol v2 codec in netty leaks native or heap memory on every connection when a client sends a syntactically valid header containing nested `PP2_TYPE_SSL` TLVs (type-length-value records) at depth two or greater. The leak occurs on the successful parse path — no exception is thrown, the message fires downstream, the decoder removes itself, and the application releases the `HAProxyMessage` normally. Yet the underlying cumulation buffer (a pooled, potentially direct `ByteBuf` allocated by the channel) remains permanently pinned. Versions 4.1.135.Final and 4.2.15.Final patch the issue.

ChromaDB · Red Hat Enterprise Linux AI (RHEL AI) 3 · Red Hat OpenShift AI (RHOAI) · Trychroma chromadb

A code injection vulnerability in version 0.4.17 or later of the ChromaDB Python project allows an authenticated attacker to run arbitrary code on the server by sending a malicious model repository and trust_remote_code set to true in the /api/v2/tenants/default_tenant/databases/default_database/collections/{collection_id} if they have the UPDATE_COLLECTION permission.

ChromaDB · Red Hat Enterprise Linux AI (RHEL AI) 3 · Red Hat OpenShift AI (RHOAI) · Trychroma chromadb

All V1 collection-level endpoints in ChromaDB's Python project pass None for the tenant and database to the authorization layer, allowing attackers to bypass authorization controls by using the V1 endpoints.

ChromaDB · Red Hat Enterprise Linux AI (RHEL AI) 3 · Red Hat OpenShift AI (RHOAI) · Trychroma chromadb

A lack of authorization validation in version 0.4.17 or later of the ChromaDB Python project allows any authenticated users to arbitrarily read, write, update, or delete data in any tenant's collection regardless of which tenant they belong to.

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.

vllm-project/vllm · Red Hat AI Inference Server · Red Hat Enterprise Linux AI (RHEL AI) 3 · Red Hat OpenShift AI (RHOAI)

vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.

js-cookie · Red Hat Cryostat 4 on RHEL 9 · Red Hat Advanced Cluster Security for Kubernetes 4.10 · Red Hat Ansible Automation Platform 2.1

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.

image-size · Red Hat Discovery 2 · Red Hat Trusted Artifact Signer 1.4 · Red Hat Gatekeeper 3

image-size through 2.0.2 contains a denial of service vulnerability that allows remote attackers to permanently block the Node.js event loop by supplying a specially crafted image buffer with a zero-valued size field in a recognized box-type. Attackers can trigger an infinite loop in the JXL or HEIF image parsers by providing a crafted image containing a box with a size of zero, causing the offset to never advance and permanently hanging the application.

Spring Micrometer · Red Hat AMQ Broker 7.13.6 · Red Hat AMQ Broker 7.14.1 · Red Hat build of Apache Camel 4.18.1.P1 for Spring Boot 3.5.16

In Micrometer, it is possible for a user to provide specially crafted HTTP requests that may cause a denial-of-service (DoS) condition. Affected versions: micrometer-core 1.16.0 through 1.16.5; 1.15.0 through 1.15.11; 1.14.0 through 1.14.15; 1.13.0 through 1.13.18; 1.9.0 through 1.9.17. micrometer-jetty11 1.16.0 through 1.16.5; 1.15.0 through 1.15.11; 1.14.0 through 1.14.15; 1.13.0 through 1.13.18. micrometer-jetty12 1.16.0 through 1.16.5; 1.15.0 through 1.15.11; 1.14.0 through 1.14.15; 1.13.0 through 1.13.18.

Spring Micrometer · Red Hat AMQ Broker 7.14.1 · Red Hat Build of Apache Camel 4.18 for Quarkus 3.33 · Red Hat build of Keycloak 26.6

In Micrometer, it is possible for a user to provide specially crafted gRPC requests that may cause a denial-of-service (DoS) condition. Affected versions: Micrometer 1.16.0 through 1.16.5; 1.15.0 through 1.15.11.

moby/v2/daemon · moby Docker Engine · docker/daemon · Red Hat Multicluster Global Hub 1.4.5

Moby is an open source container framework. In versions prior to 29.5.1 and in moby/moby v2 prior to v2.0.0-beta.14, when a compressed archive is uploaded to a container via `PUT /containers/{id}/archive` or piped through `docker cp -`, the daemon resolves decompression binaries (such as `xz` or `unpigz`) from the container's filesystem rather than the host's due to incorrect ordering of operations. A malicious container image containing a trojanized decompression binary can achieve arbitrary code execution with full daemon privileges, including host root UID and unrestricted capabilities, when a user uploads a compressed (xz or gzip) archive into that container. This issue is fixed in Docker Engine 29.5.1 and moby/moby v2.0.0-beta.14. Workarounds include only running containers from trusted images, using authorization plugins to restrict access to the `PUT /containers/{id}/archive` endpoint, and avoiding piping compressed archives into containers created from untrusted images

mlflow/mlflow · Red Hat OpenShift AI (RHOAI) · Lfprojects mlflow

A vulnerability in mlflow/mlflow versions prior to 3.11.0 allows for the resolution of environment variables in AI Gateway secrets, which can be exploited to exfiltrate sensitive server-side environment credentials to an attacker-controlled endpoint. This issue arises because the `api_key` field in gateway secrets can accept `$ENV_VAR` references, which are resolved against the MLflow server's environment during runtime. The resolved secrets are then sent in provider authentication headers to the configured upstream `api_base`. This vulnerability can be exploited by low-privileged authenticated users in basic-auth deployments or by unauthenticated users in default deployments without `basic-auth`. The impact includes potential leakage of sensitive credentials such as cloud artifact credentials (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`), which could lead to artifact poisoning and cross-boundary code execution in downstream environments. The issue is fixed in version 3.11.0.

vitejs launch-editor · vitejs vite · Red Hat Cluster Observability Operator 1.5.0 · Red Hat Cryostat 4

launch-editor allows users to open files with line numbers in editor from Node.js. Prior to version 2.9.0, due to the insufficient sanitization of the `file` argument in the `launchEditor`, an attacker can execute arbitrary commands on Windows by supplying a filename that contains special characters. This issue has been fixed in the `launch-editor` version 2.9.0, corresponding to vite version 5.4.9.

sebhildebrandt systeminformation · Red Hat Developer Hub 1.9 · Red Hat OpenShift AI 3.4 · Red Hat Cryostat 4

systeminformation is a System and OS information library for node.js. From 4.17.0 to 5.31.5, on Linux, systeminformation is vulnerable to command injection in networkInterfaces() when an active NetworkManager connection profile name contains shell metacharacters. The vulnerable value is obtained internally from real nmcli device status output. The library sanitizes the network interface name before using it in shell commands, but it does not apply equivalent sanitization to the parsed NetworkManager connection profile name. That unsanitized connectionName is then interpolated into three shell command strings executed through execSync(). This vulnerability is fixed in 5.31.6.

BerriAI litellm · Red Hat Exploit Intelligence · Red Hat Ansible Automation Platform 2 · Red Hat OpenShift AI (RHOAI)

LiteLLM prior to 1.83.10 allows a user to modify their own user_role via the /user/update endpoint. While the endpoint correctly restricts users to updating only their own account, it does not restrict which fields may be changed. A user who can reach this endpoint can set their role to proxy_admin, gaining full administrative access to LiteLLM including all users, teams, keys, models, and prompt history. Users with the org_admin role have legitimate access to this endpoint and can exploit this vulnerability without chaining any additional flaw.

BerriAI litellm · Red Hat Exploit Intelligence · Red Hat Ansible Automation Platform 2 · Red Hat OpenShift AI (RHOAI)

LiteLLM prior to 1.83.14 allows an authenticated internal_user to create API keys with access to routes that their role does not permit. When generating a key, the allowed_routes field is stored without verifying that the specified routes fall within the user's own permissions. A key created with access to admin-only routes can then be used to reach those routes successfully, bypassing the role-based access controls that would otherwise block the request, enabling full privilege escalation from internal_user to proxy_admin.

Red Hat OpenShift AI (RHOAI)

An issue was discovered in ModelScope 1.25.0 allowing attackers to execute arbitrary code via crafted module listed in the configuration file (dey_mini.yaml) under the key ['nnet']['module'].

huggingface diffusers · Red Hat OpenShift AI 3.4 · Red Hat AI Inference Server · Red Hat Enterprise Linux AI (RHEL AI) 3

Diffusers is the a library for pretrained diffusion models. Prior to 0.38.0, a trust_remote_code bypass in DiffusionPipeline.from_pretrained allows arbitrary remote code execution despite the user passing trust_remote_code=False (or omitting it, which is the default). The vulnerability has three variants, all sharing the same root cause — the trust_remote_code gate was implemented inside DiffusionPipeline.download() rather than at the actual dynamic-module load site, so any code path that bypassed or short-circuited download() also bypassed the security check. DiffusionPipeline.from_pretrained('repoA', custom_pipeline='attacker/repoB', trust_remote_code=False) — the gate evaluated against repoA's file list rather than repoB's, so repoB's pipeline.py was loaded and executed. DiffusionPipeline.from_pretrained('/local/snapshot', custom_pipeline='attacker/repoB', trust_remote_code=False) — the local-path branch never invoked download(), so the gate was never reached and remote code from repoB executed. DiffusionPipeline.from_pretrained('/local/snapshot', trust_remote_code=False) where the snapshot contains custom component files (e.g. unet/my_unet_model.py) referenced from model_index.json — same root cause; the local path skipped download() and custom component code executed. This vulnerability is fixed in 0.38.0.

Kludex python-multipart · Red Hat Ansible Automation Platform 2.6 · Red Hat Ansible Automation Platform 2.7 · Red Hat Migration Toolkit for Applications 8.2

Python-Multipart is a streaming multipart parser for Python. Prior to 0.0.27, python-multipart has a denial of service vulnerability in multipart part header parsing. When parsing multipart/form-data, MultipartParser previously had no limit on the number of part headers or the size of an individual part header. An attacker could send a request with either many repeated headers without terminating the header block or a single very large header value, causing excessive CPU work before request rejection or completion. This vulnerability is fixed in 0.0.27.

protobufjs protobuf.js · Red Hat Ansible Automation Platform 2.6 for RHEL 9 · Red Hat Enterprise Linux 10 · Red Hat Ansible Automation Platform 2.6

protobufjs compiles protobuf definitions into JavaScript (JS) functions. Prior to 7.5.6 and 8.0.2, protobufjs generated JavaScript for toObject conversion could include an unsafe expression derived from a schema-controlled bytes field default value. A crafted descriptor with a non-string default value for a bytes field could cause attacker-controlled code to be emitted into the generated conversion function. This vulnerability is fixed in 7.5.6 and 8.0.2.

protobufjs protobuf.js · Red Hat Ansible Automation Platform 2.2 · Red Hat OpenShift AI 3.4 · Red Hat OpenShift Pipelines

protobufjs compiles protobuf definitions into JavaScript (JS) functions. Prior to 7.5.6 and 8.0.2, protobufjs could recurse without a depth limit while decoding nested protobuf data. This affected both skipping unknown group fields and generated decoding of nested message fields. A crafted protobuf binary payload could cause the JavaScript call stack to be exhausted during decoding. This vulnerability is fixed in 7.5.6 and 8.0.2.

jupyterlab · Red Hat Migration Toolkit for Applications 8.2 · Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI (RHOAI)

JupyterLab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. From 4.0.0 to 4.5.6, the allow-list of extensions that can be installed from PyPI Extension Manager (allowed_extensions_uris) is not correctly enforced by JupyterLab. The PyPI Extension Manager was not contained to packages listed on the default PyPI index. This vulnerability is fixed in 4.5.7.

mlflow/mlflow · Red Hat OpenShift AI 3.3 · Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI (RHOAI)

A vulnerability in the `_create_model_version()` handler of `mlflow/server/handlers.py` in mlflow/mlflow versions 3.9.0 and earlier allows an unauthenticated remote attacker to read arbitrary files from the server's filesystem. The issue arises when a `CreateModelVersion` request includes the tag `mlflow.prompt.is_prompt`, which bypasses source path validation. This enables an attacker to store an arbitrary local filesystem path as the model version source. The `get_model_version_artifact_handler()` function later uses this source to serve files without verifying the model version's prompt status, leading to a complete confidentiality compromise. This issue is fixed in version 3.10.0.

argoproj argo-workflows · Red Hat OpenShift AI (RHOAI) · Argoproj argo workflows

Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. From version 4.0.0 to before version 4.0.5, the Sync Service's ConfigMap-backed provider (server/sync/sync_cm.go) performs zero authorization checks on all CRUD operations (create, read, update, delete). Any authenticated user — including those using fake Bearer tokens — can create, read, update, and delete Kubernetes ConfigMaps containing synchronization limits. This issue has been patched in version 4.0.5.

argoproj argo-workflows · Red Hat OpenShift AI (RHOAI) · Argoproj argo workflows

Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Prior to versions 3.7.14 and 4.0.5, a user with create Workflow permission can bypass templateReferencing: Strict to get host network access, switch service accounts, override pod security context, add tolerations to schedule on control-plane nodes, or enable SA token mounting. This defeats the stated purpose of the feature. The practical impact depends on what Kubernetes-level controls are in place. Clusters with PodSecurity admission or OPA/Gatekeeper would independently block some of these (like hostNetwork). Clusters that rely on Argo's Strict mode as the primary enforcement layer are fully exposed. This issue has been patched in versions 3.7.14 and 4.0.5.

argoproj argo-workflows · Red Hat OpenShift AI (RHOAI) · Argoproj argo workflows

Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Prior to versions 3.7.14 and 4.0.5, the Webhook Interceptor loads the entire request body into memory before authenticating the request or verifying its signature. This occurs on the /api/v1/events/ endpoint, which is publicly accessible (albeit intended for webhooks). An attacker can send a request with an extremely large body (e.g., multiple gigabytes), causing the Argo Server to allocate excessive memory, potentially leading to an Out-Of-Memory (OOM) crash and denial of service. This issue has been patched in versions 3.7.14 and 4.0.5.

BerriAI litellm · Red Hat OpenShift AI 2.25 · Red Hat OpenShift AI 3.3 · Red Hat OpenShift AI 3.4

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.

BerriAI litellm · Red Hat Exploit Intelligence · Red Hat Ansible Automation Platform 2 · Red Hat OpenShift AI (RHOAI)

LiteLLM is a proxy server (AI Gateway) to call LLM APIs in OpenAI (or native) format. From version 1.80.5 to before version 1.83.7, the POST /prompts/test endpoint accepted user-supplied prompt templates and rendered them without sandboxing. A crafted template could run arbitrary code inside the LiteLLM Proxy process. The endpoint only checks that the caller presents a valid proxy API key, so any authenticated user could reach it. Depending on how the proxy is deployed, this could expose secrets in the process environment (such as provider API keys or database credentials) and allow commands to be run on the host. This issue has been patched in version 1.83.7.

josdejong mathjs · Red Hat Cryostat 4 · Red Hat Developer Hub · Red Hat Enterprise Linux 10

Math.js is an extensive math library for JavaScript and Node.js. From version 13.1.0 to before version 15.2.0, arbitrary JavaScript can be executed via the expression parser of mathjs. This issue has been patched in version 15.2.0.

xmldom · Red Hat Developer Hub 1.9 · Red Hat OpenShift AI 3.4 · Red Hat Build of Podman Desktop

xmldom is a pure JavaScript W3C standard-based (XML DOM Level 2 Core) `DOMParser` and `XMLSerializer` module. In @xmldom/xmldom prior to versions 0.9.10 and 0.8.13 and xmldom version 0.6.0 and prior, the package allows attacker-controlled processing instruction data to be serialized into XML without validating or neutralizing the PI-closing sequence ?>. As a result, an attacker can terminate the processing instruction early and inject arbitrary XML nodes into the serialized output. This issue has been patched in versions @xmldom/xmldom versions 0.9.10 and 0.8.13.

xmldom · Red Hat Developer Hub 1.8 · Red Hat Developer Hub 1.9 · Red Hat OpenShift Container Platform 4.20

xmldom is a pure JavaScript W3C standard-based (XML DOM Level 2 Core) `DOMParser` and `XMLSerializer` module. In @xmldom/xmldom prior to versions 0.9.10 and 0.8.13 and xmldom version 0.6.0 and prior, the package serializes DocumentType node fields (internalSubset, publicId, systemId) verbatim without any escaping or validation. When these fields are set programmatically to attacker-controlled strings, XMLSerializer.serializeToString can produce output where the DOCTYPE declaration is terminated early and arbitrary markup appears outside it. This issue has been patched in versions @xmldom/xmldom versions 0.9.10 and 0.8.13.

xmldom · Red Hat Developer Hub 1.9 · Red Hat OpenShift AI 3.4 · Red Hat Build of Podman Desktop

xmldom is a pure JavaScript W3C standard-based (XML DOM Level 2 Core) `DOMParser` and `XMLSerializer` module. In @xmldom/xmldom prior to versions 0.9.10 and 0.8.13 and xmldom version 0.6.0 and prior, seven recursive traversals in lib/dom.js operate without a depth limit. A sufficiently deeply nested DOM tree causes a RangeError: Maximum call stack size exceeded, crashing the application. This issue has been patched in versions @xmldom/xmldom versions 0.9.10 and 0.8.13.

xmldom · Red Hat Developer Hub 1.9 · Red Hat OpenShift AI 3.4 · Red Hat Build of Podman Desktop

xmldom is a pure JavaScript W3C standard-based (XML DOM Level 2 Core) `DOMParser` and `XMLSerializer` module. In @xmldom/xmldom prior to versions 0.9.10 and 0.8.13 and xmldom version 0.6.0 and prior, the package allows attacker-controlled comment content to be serialized into XML without validating or neutralizing comment-breaking sequences. As a result, an attacker can terminate the comment early and inject arbitrary XML nodes into the serialized output. This issue has been patched in versions @xmldom/xmldom versions 0.9.10 and 0.8.13.

lepture mistune · Red Hat Migration Toolkit for Applications 8.2 · Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI (RHOAI)

In versions 3.0.0a1 through 3.2.0 of Mistune, there is a ReDoS (Regular Expression Denial of Service) vulnerability in `LINK_TITLE_RE` that allows an attacker who can supply Markdown for parsing to cause denial of service. The regular expression used for parsing link titles contains overlapping alternatives that can trigger catastrophic backtracking. In both the double-quoted and single-quoted branches, a backslash followed by punctuation can be matched either as an escaped punctuation sequence or as two ordinary characters, creating an ambiguous pattern inside a repeated group. If an attacker supplies Markdown containing repeated ! sequences with no closing quote, the regex engine explores an exponential number of backtracking paths. This is reachable through normal Markdown parsing of inline links and block link reference definitions. A small crafted input can therefore cause significant CPU consumption and make applications using Mistune unresponsive.

jupyter-server jupyter_server · Red Hat Migration Toolkit for Applications 8.2 · Red Hat OpenShift AI (RHOAI) · Jupyter jupyter server

Jupyter Server is the backend for Jupyter web applications. In versions 2.17.0 and earlier, the Origin header validation uses Python's re.match() to check incoming origins against the allow_origin_pat configuration value. Because re.match() only anchors at the start of the string and does not require a full match, a pattern intended to match only a trusted domain (e.g., trusted.example.com) will also match any origin that begins with that domain followed by additional characters (e.g., trusted.example.com.evil.com). An attacker who controls such a domain can bypass the CORS origin restriction and make cross-origin requests to the Jupyter Server API from an untrusted site. This issue has been fixed in version 2.18.0.

quarkusio quarkus · Red Hat Cryostat 4 on RHEL 9 · Red Hat HawtIO HawtIO 4.4.0 · Red Hat Build of Apache Camel 4.14 for Quarkus 3.27

Quarkus is a Java framework for building cloud-native applications. In versions prior to 3.20.6.1, 3.27.3.1, 3.33.1.1, 3.35.1.1, 3.34.7, and 3.35.2, a path normalization inconsistency between the security layer and the routing layer allows unauthenticated or lower-privileged users to bypass HTTP path-based authorization policies. Quarkus's security layer performs authorization checks on the raw URL path which preserves matrix parameters (semicolons), while RESTEasy Reactive's routing layer strips matrix parameters before matching endpoints. An attacker can append a semicolon and arbitrary text to a request URL (e.g., /api/admin;anything) to bypass policies protecting /api/admin while still routing to the protected endpoint. This issue has been fixed in versions 3.20.6.1, 3.27.3.1, 3.33.1.1, 3.35.1.1, 3.34.7, and 3.35.2.

jupyter-server jupyter_server · Red Hat Migration Toolkit for Applications 8.2 · Red Hat OpenShift AI 3.4 · Red Hat OpenShift AI (RHOAI)

Jupyter Server is the backend for Jupyter web applications. In versions 2.17.0 and earlier, a path traversal vulnerability in the REST API allows an authenticated user to escape the configured root_dir and access sibling directories whose names begin with the same prefix as the root_dir. For example, with a root_dir named "test", the API permits access to a sibling directory named "testtest" through a crafted request to the /api/contents endpoint using encoded path components. An attacker can read, write, and delete files in affected sibling directories. Multi-tenant deployments using predictable naming schemes are particularly at risk, as a user with a directory named "user1" could access directories for user10 through user19 and beyond. A user who can choose a single-character folder name could gain access to a significant number of sibling directories. Version 2.18.0 contains a fix. As a workaround, ensure folder names do not share a common prefix with any sibling directory.

Apache Software Foundation Apache Thrift · Red Hat Cryostat 4 on RHEL 9 · Red Hat Build of Apache Camel 3.33 for Quarkus 3.33.2.SP1 · Red Hat Multicluster Global Hub 1.3.4

Improper Validation of Certificate with Host Mismatch vulnerability in Apache Thrift. This issue affects Apache Thrift: before 0.23.0. Users are recommended to upgrade to version 0.23.0, which fixes the issue.

Apache Software Foundation Apache OpenNLP · Red Hat Data Grid 8 · Red Hat Fuse 7 · Red Hat JBoss Enterprise Application Platform Expansion Pack

OOM Denial of Service via Unbounded Array Allocation in Apache OpenNLP AbstractModelReader  Versions Affected:  before 1.9.5 before 2.5.9 before 3.0.0-M3  Description: The AbstractModelReader methods getOutcomes(), getOutcomePatterns(), and getPredicates() each read a 32-bit signed integer count field from a binary model stream and pass that value directly to an array allocation (new String[numOutcomes], new int[numOCTypes][], new String[NUM_PREDS]) without validating that the value is non-negative or within a reasonable bound. The count is therefore fully attacker-controlled when the model file originates from an untrusted source. A crafted .bin model file in which any of these count fields is set to Integer.MAX_VALUE (or any value large enough to exhaust the available heap) triggers an OutOfMemoryError at the array allocation itself, before the corresponding label or pattern data is consumed from the stream. The error occurs very early in deserialization: for a GIS model, getOutcomes() is reached after only the model-type string, the correction constant, and the correction parameter have been read; so the attacker pays no meaningful size cost to weaponize a payload, and a single small file can crash a JVM that loads it. Any code path that deserializes a .bin model is affected, including direct use of GenericModelReader and any higher-level component that delegates to it during model load. The practical impact is denial of service against processes that load model files from untrusted or semi-trusted origins.   Mitigation: * 2.x users should upgrade to 2.5.9. * 3.x users should upgrade to 3.0.0-M3. Note: The fix introduces an upper bound on each of the three count fields, checked before array allocation; counts that are negative or exceed the bound cause an IllegalArgumentException to be thrown and the read to fail fast with no large allocation. The default bound is 10,000,000, which is well above the entry counts of legitimate OpenNLP models but far below any value that would threaten heap exhaustion. Deployments that legitimately need to load models with more entries than the default can raise the limit at JVM startup by setting the OPENNLP_MAX_ENTRIES system property to the desired positive integer (e.g. -DOPENNLP_MAX_ENTRIES=50000000); invalid or non-positive values fall back to the default. Users who cannot upgrade immediately should treat all .bin model files as untrusted input unless their provenance is verified, and should avoid loading models supplied by end users or fetched from third-party repositories without integrity checks.

Apache Software Foundation Apache Thrift · Red Hat Multicluster Global Hub 1.3.4 · Red Hat Multicluster Global Hub 1.4.5 · Red Hat Multicluster Global Hub 1.6.5

Integer Overflow or Wraparound vulnerability in Apache Thrift. This issue affects Apache Thrift: before 0.23.0. Users are recommended to upgrade to version 0.23.0, which fixes the issue.

Apache Software Foundation Apache Thrift · Red Hat Multicluster Global Hub 1.3.4 · Red Hat Multicluster Global Hub 1.4.5 · Red Hat Multicluster Global Hub 1.6.5

Out-of-bounds Read vulnerability in Apache Thrift. This issue affects Apache Thrift: before 0.23.0. Users are recommended to upgrade to version 0.23.0, which fixes the issue.

Apache Software Foundation Apache Thrift · Red Hat Multicluster Global Hub 1.3.4 · Red Hat Multicluster Global Hub 1.4.5 · Red Hat Multicluster Global Hub 1.6.5

Integer Overflow or Wraparound vulnerability in Apache Thrift TFramedTransport Go language implementation This issue affects Apache Thrift: before 0.23.0. Users are recommended to upgrade to version 0.23.0, which fixes the issue.

Apache Software Foundation Apache Thrift · Red Hat Cryostat 4 on RHEL 9 · Red Hat Advanced Cluster Management for Kubernetes 2.14 · Red Hat Advanced Cluster Management for Kubernetes 2.15

Mismatched Memory Management Routines vulnerability in Apache Thrift c_glib language bindings. This issue affects Apache Thrift: before 0.23.0. Users are recommended to upgrade to version 0.23.0, which fixes the issue. Description: Specially crafted requests can crash an c_glib-based Thrift server with a clean but fatal "free(): invalid pointer" error message.

BerriAI litellm · Red Hat Ansible Automation Platform 2.6 · Red Hat OpenShift AI 2.25 · Red Hat OpenShift AI 3.3

LiteLLM is a proxy server (AI Gateway) to call LLM APIs in OpenAI (or native) format. Prior to 1.83.0, the /config/update endpoint does not enforce admin role authorization. A user who is already authenticated into the platform can then use this endpoint to modify proxy configuration and environment variables, register custom pass-through endpoint handlers pointing to attacker-controlled Python code, achieving remote code execution, read arbitrary server files by setting UI_LOGO_PATH and fetching via /get_image, and take over other privileged accounts by overwriting UI_USERNAME and UI_PASSWORD environment variables. Fixed in v1.83.0.