Red Hat OpenShift AI 3.3 vulnerabilities
71 known vulnerabilities in Red Hat OpenShift AI 3.3 from Red Hat. 70 are rated high or critical, and 2 have been exploited in real attacks. Each entry lists the affected versions and the release that fixes it, where the vendor has published one.
Latest first
- 8.8 High severity
Red Hat OpenShift AI 2.25: Execution with Unnecessary Privileges
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, a
- 8.8 High severity
Red Hat OpenShift AI 3.3: Privilege Defined With Unsafe Actions
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` perm
- 8.8 High severity
Red Hat OpenShift AI 2.25: Incorrect Privilege Assignment
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 hi
- 8.8 High severity
Red Hat OpenShift AI 2.25: Execution with Unnecessary Privileges
A flaw was found in odh-dashboard. This vulnerability allows an attacker, who has compromised the dashboard's Service Account (SA) token, to exploit overly broad permissions granted to the SA. This enables the attacker to escalate their privileges to cluster-a
- 9.9 Critical severity
Red Hat OpenShift AI 2.25: Deserialization of Untrusted Data
A flaw was found in Feast. The system improperly deserializes user-defined functions (UDFs) stored in its registry, which are serialized using the 'dill' library. This allows a remote attacker to store a malicious UDF, leading to unauthenticated arbitrary code
- 8.5 High severity
Red Hat OpenShift AI 2.25: Missing Authorization
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. Th
- 7.7 High severity
Red Hat OpenShift AI 2.25: Missing Authentication for Critical Functionality
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, registr
- 7.6 High severity
Red Hat OpenShift AI 2.25: Incorrect Privilege Assignment
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, ac
- 7.1 High severity
Red Hat OpenShift AI 2.25: Authorization Bypass Through User-Controlled Key
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
- 7.5 High severity
Red Hat OpenShift AI 2.25: Allocation of Resources Without Limits or Throttling
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
- 8.8 High severity
Red Hat OpenShift AI 2.25: Improperly Controlled Modification of Dynamically-Determined Object Attributes
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
- 7.5 High severity
Red Hat OpenShift AI 2.25: Use of Cryptographically Weak Pseudo-Random Number Generator (PRNG)
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 Serv
- 8.7 High severity
Red Hat OpenShift AI 2.25: Execution with Unnecessary Privileges
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 com
- 8.0 High severity
Red Hat OpenShift AI 2.25: Missing Authentication for Critical Functionality
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 a
- 8.1 High severity
Red Hat OpenShift AI 2.25: Incorrect Privilege Assignment
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 execut
- 8.8 High severity
Red Hat OpenShift AI 2.25: Origin Validation Error
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
- 7.1 High severity
xdgmime: Heap-based Buffer Overflow
A flaw was found in xdgmime. A heap-based buffer overflow can be triggered in _xdg_mime_magic_parse_magic_line() in the xdgmimemagic.c file on little-endian systems when an attacker-controlled MIME magic file in a user-writable XDG data location (e.g., in the
- 9.3 Critical severity
Red Hat OpenShift AI 2.25: Server-Side Request Forgery
A flaw was found in the `guardrails-detectors` component. This vulnerability allows a remote attacker to perform a blind Server-Side Request Forgery (SSRF) by submitting a specially crafted XML Schema Definition (XSD) string. This can lead to unauthorized acce
- 7.1 High severity
Red Hat Enterprise Linux AI 3.3 for RHEL 9: Out-of-bounds Write
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 ove
- 7.1 High severity
Red Hat Enterprise Linux AI 3.3 for RHEL 9: Out-of-bounds Read
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 laye
- 7.1 High severity
Red Hat Enterprise Linux AI 3.3 for RHEL 9: Out-of-bounds Write
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
- 7.6 High severity
Red Hat Enterprise Linux 10.0 Extended Update…: Heap-based Buffer Overflow
A heap buffer overflow vulnerability was found in libaom, the reference AV1 codec implementation. A flaw in the AV1 encoder's Look-Ahead Processing (LAP) mode causes the first-pass stats ring buffer wrap-around guard to be bypassed when g_lag_in_frames is set
- 9.6 Critical severity
huggingface/transformers: Inclusion of Functionality from Untrusted Control Sphere
A vulnerability in the LightGlue model loading path of huggingface/transformers version 5.2.0 allows an attacker-controlled model repository to execute arbitrary code during model initialization. The issue arises because the `trust_remote_code` parameter, inte
- 7.3 High severity
aiohttp: Deserialization of Untrusted Data
AIOHTTP is an asynchronous HTTP client/server framework for asyncio and Python. Prior to version 3.14.0, using ``CookieJar.load()`` with untrusted input may allow arbitrary code execution. Most applications using this function will be doing so with the user's
- 7.4 High severity
pyjwt: Improper Authentication
PyJWT is a JSON Web Token implementation in Python. Prior to 2.13.0, when the verifier is decoding JSON Web Tokens, while supporting both asymmetric and HMAC algorithms, the library does not validate use of JSON Web Keys in HMAC algorithm, allowing attacker to
- 6.5 Medium severity
Kludex Starlette HTTP Request/Response Smuggling Exploited
Starlette is a lightweight ASGI framework/toolkit. Prior to version 1.0.1, the HTTP `Host` request header was not validated before being used to reconstruct `request.url`. Because the routing algorithm relies on the raw HTTP path while `request.url` is rebuilt
- 7.1 High severity
golang.org/x/crypto/ssh: Incorrect Authorization
Previously, CVE-2024-45337 fixed an authorization bypass for misused ssh server configurations; if any other type of callback is passed other than public key, then the source-address validation would be skipped.
- 7.5 High severity
golang.org/x/crypto/ssh: Improper Restriction of Operations within the Bounds of a Memory Buffer
A malicious SSH peer could send unsolicited global request responses to fill an internal buffer, blocking the connection's read loop. The blocked goroutine could not be released by calling Close(), resulting in a resource leak per connection. Unsolicited globa
- 7.5 High severity
python-multipart: Allocation of Resources Without Limits or Throttling
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 pa
- 7.5 High severity
urllib3: Improper Handling of Highly Compressed Data (Data Amplification)
urllib3 is an HTTP client library for Python. From 2.6.0 to before 2.7.0, urllib3 could decompress the whole response instead of the requested portion (1) during the second HTTPResponse.read(amt=N) call when the response was decompressed using the official Bro
- 7.5 High severity
mlflow/mlflow: Improper Limitation of a Pathname to a Restricted Directory
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 `CreateM
- 8.8 High severity
BerriAI LiteLLM Command Injection Exploited
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
- 7.5 High severity
follow-redirects: Exposure of Sensitive Information to an Unauthorized Actor
follow-redirects is an open source, drop-in replacement for Node's `http` and `https` modules that automatically follows redirects. Prior to 1.16.0, when an HTTP request follows a cross-domain redirect (301/302/307/308), follow-redirects only strips authorizat
- 9.8 Critical severity
protobuf.js: Improper Control of Generation of Code
protobufjs compiles protobuf definitions into JavaScript (JS) functions. In versions prior to 8.0.1, 7.5.5, and 6.11.6, attackers can inject arbitrary code in the "type" fields of protobuf definitions, which will then execute during object decoding using that
- 7.5 High severity
Pillow: Uncontrolled Resource Consumption
Pillow is a Python imaging library. Versions 10.3.0 through 12.1.1 did not limit the amount of GZIP-compressed data read when decoding a FITS image, making them vulnerable to decompression bomb attacks. A specially crafted FITS file could cause unbounded memor
- 7.8 High severity
keras-team/keras: Deserialization of Untrusted Data
A vulnerability in the `TFSMLayer` class of the `keras` package, version 3.13.0, allows attacker-controlled TensorFlow SavedModels to be loaded during deserialization of `.keras` models, even when `safe_mode=True`. This bypasses the security guarantees of `saf
- 9.9 Critical severity
Red Hat OpenShift AI 2.16: Insertion of Sensitive Information Into Sent Data
A flaw was found in odh-dashboard in Red Hat Openshift AI. This vulnerability in the `odh-dashboard` component of Red Hat OpenShift AI (RHOAI) allows for the disclosure of Kubernetes Service Account tokens through a NodeJS endpoint. This could enable an attack
- 9.9 Critical severity
axios: Unintended Proxy or Intermediary
Axios is a promise based HTTP client for the browser and Node.js. Prior to 1.15.0 and 0.31.0, Axios does not correctly handle hostname normalization when checking NO_PROXY rules. Requests to loopback addresses like localhost. (with a trailing dot) or [::1] (IP
- 9.8 Critical severity
cryptography: Improper Restriction of Operations within the Bounds of a Memory Buffer
cryptography is a package designed to expose cryptographic primitives and recipes to Python developers. From 45.0.0 to before 46.0.7, if a non-contiguous buffer was passed to APIs which accepted Python buffers (e.g. Hash.update()), this could lead to buffer ov
- 9.1 Critical severity
litellm: Improper Authentication
LiteLLM is a proxy server (AI Gateway) to call LLM APIs in OpenAI (or native) format. Prior to 1.83.0, when JWT authentication is enabled (enable_jwt_auth: true), the OIDC userinfo cache uses token[:20] as the cache key. JWT headers produced by the same signin