| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| A flaw was found in `guardrails-detectors`, a component of Red Hat OpenShift AI. This vulnerability, known as Regular Expression Denial of Service (ReDoS), allows a remote attacker to provide specially crafted regular expressions to the public detection API. This can cause catastrophic backtracking, leading to a worker process consuming 100% CPU indefinitely and resulting in a denial of service for the entire guardrails-mediated LLM pipeline. |
| A flaw was found in the gorch service template, which is part of the trustyai-service-operator. Even when authentication is enabled, the gorch service exposes unproxied orchestrator and detector metrics ports. This allows any pod on the cluster network to directly access these ports, bypassing the kube-rbac-proxy and its authentication mechanisms. This could lead to unauthorized access to the orchestrator and detector metrics. |
| A flaw was found in the TrustyAI Service Operator. When deploying services like gorch or NemoGuardrails, if a specific security setting is not enabled, these services can expose their communication channels without requiring users to prove their identity. This allows any other program within the cluster to access the AI guardrails and orchestrator without proper authorization. An attacker could exploit this to gain unauthorized access to sensitive information and potentially make limited changes to the AI models. |
| 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. |
| 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). |
| 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. |
| 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 to 1 or higher. This results in a 232-byte out-of-bounds write on every encoded frame after the second, corrupting adjacent heap objects. An attacker who can influence encoder configuration in a transcoding service or WebRTC session could exploit this to cause a denial of service (process crash) or potentially achieve code execution. |
| A vulnerability has been identified in the Feast Feature Server’s `/save-document` endpoint that allows an unauthenticated remote attacker to write arbitrary JSON files to the server's filesystem. Although the system attempts to restrict file locations, these protections can be bypassed, enabling an attacker to overwrite vital application configurations or startup scripts. Because this flaw requires no credentials or special privileges, any attacker with network access to the server can potentially compromise the integrity of the system. This could lead to unauthorized system modifications, denial of service through disk exhaustion, or potential remote code execution. |
| In crossbeam-channel rust crate, the internal `Channel` type's `Drop` method has a race condition which could, in some circumstances, lead to a double-free that could result in memory corruption. |
| A flaw was found in npm-serialize-javascript. The vulnerability occurs because the serialize-javascript module does not properly sanitize certain inputs, such as regex or other JavaScript object types, allowing an attacker to inject malicious code. This code could be executed when deserialized by a web browser, causing Cross-site scripting (XSS) attacks. This issue is critical in environments where serialized data is sent to web clients, potentially compromising the security of the website or web application using this package. |
| A flaw was found in github.com/go-viper/mapstructure/v2, in the field processing component using mapstructure.WeakDecode. This vulnerability allows information disclosure through detailed error messages that may leak sensitive input values via malformed user-supplied data processed in security-critical contexts. |
| A use-after-free vulnerability was found in FFmpeg's RASC video decoder. The decode_move() function initializes a read pointer into a decompressed buffer, but a subsequent reallocation of that same buffer during move-table processing leaves the pointer dangling. An attacker could exploit this by providing a specially crafted AVI file containing a malicious RASC video stream. When a user opens or plays the file, the decoder reads from freed heap memory, which could lead to a denial of service (crash). |
| A flaw was found in vLLM, an open-source library for large language model inference. This vulnerability arises from improper handling of image metadata, specifically EXIF orientation and PNG transparency (tRNS) data, during image processing. When images are converted to RGB, transparency information may be implicitly discarded or remapped, leading to unexpected rendering of transparent pixels and distortion of input content. This can result in the model misinterpreting image content, potentially affecting the integrity of processed 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 own data, so this is unlikely to affect many applications. Version 3.14.0 patches the issue. If an application does allow attacker controlled files to be loaded, a workaround on older releases would be to sanitize the files before loading. |
| The net/http package improperly accepts a bare LF as a line terminator in chunked data chunk-size lines. This can permit request smuggling if a net/http server is used in conjunction with a server that incorrectly accepts a bare LF as part of a chunk-ext. |
| RADIUS Protocol under RFC 2865 is susceptible to forgery attacks by a local attacker who can modify any valid Response (Access-Accept, Access-Reject, or Access-Challenge) to any other response using a chosen-prefix collision attack against MD5 Response Authenticator signature. |
| A flaw was found in Hibernate. A remote attacker with low privileges could exploit a second-order SQL injection vulnerability by providing specially crafted, unsanitized non-alphanumeric characters in the ID column when the InlineIdsOrClauseBuilder is used. This could lead to sensitive information disclosure, such as reading system files, and allow for data manipulation or deletion within the application's database, resulting in an application level denial of service. |
| A flaw was found in Red Hat OpenShift AI (RHOAI) llama-stack-operator. This vulnerability allows unauthorized access to Llama Stack services deployed in other namespaces via direct network requests, because no NetworkPolicy restricts access to the llama-stack service endpoint. As a result, a user in one namespace can access another user’s Llama Stack instance and potentially view or manipulate sensitive data. |
| A flaw was found in Red Hat Openshift AI Service. The TrustyAI component is granting all service accounts and users on a cluster permissions to get, list, watch any pod in any namespace on the cluster.
TrustyAI is creating a role `trustyai-service-operator-lmeval-user-role` and a CRB `trustyai-service-operator-default-lmeval-user-rolebinding` which is being applied to `system:authenticated` making it so that every single user or service account can get a list of pods running in any namespace on the cluster
Additionally users can access all `persistentvolumeclaims` and `lmevaljobs` |
| A flaw was found in odh-dashboard in Red Hat Openshift AI. This vulnerability in the `odh-dashboard` component of Red Hat OpenShift AI (RHOAI) allows for the disclosure of Kubernetes Service Account tokens through a NodeJS endpoint. This could enable an attacker to gain unauthorized access to Kubernetes resources. |
