| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| A stack buffer overflow was found in Internationl components for unicode (ICU ). While running the genrb binary, the 'subtag' struct overflowed at the SRBRoot::addTag function. This issue may lead to memory corruption and local arbitrary code execution. |
| A flaw was found in gnutls. Servers configured with RSA-PSK (Rivest–Shamir–Adleman – Pre-Shared Key) wrongfully matched usernames containing a NUL character with truncated usernames. A remote attacker could exploit this by sending a specially crafted username, leading to an authentication bypass. This vulnerability allows an attacker to gain unauthorized access by circumventing the authentication process. |
| A flaw was found in gnutls. A remote attacker could exploit an issue in the Datagram Transport Layer Security (DTLS) packet reordering logic. The comparator function, responsible for ordering DTLS packets by sequence numbers, did not correctly handle packets with duplicate sequence numbers. This could lead to unstable packet ordering or undefined behavior, resulting in a denial of service. |
| A heap buffer overflow vulnerability exists in the DTLS handshake fragment reassembly logic of GnuTLS. The issue arises in merge_handshake_packet() where incoming handshake fragments are matched and merged based solely on handshake type, without validating that the message_length field remains consistent across all fragments of the same logical message. An attacker can exploit this by sending crafted DTLS fragments with conflicting message_length values, causing the implementation to allocate a buffer based on a smaller initial fragment and subsequently write beyond its bounds using larger, inconsistent fragments. Because the merge operation does not enforce proper bounds checking against the allocated buffer size, this results in an out-of-bounds write on the heap. The vulnerability is remotely exploitable without authentication via the DTLS handshake path and can lead to application crashes or potential memory corruption. |
| A flaw in GnuTLS DTLS handshake parsing allows malformed fragments with zero length and non-zero offset, leading to an integer underflow during reassembly and resulting in an out-of-bounds read. This issue is remotely exploitable and may cause information disclosure or denial of service. |
| A flaw was found in FFmpeg. The tdsc_load_cursor() function writes beyond
the bounds of a heap-allocated buffer when processing crafted TDSC cursor
data. A remote attacker could exploit this by supplying a specially crafted
video file, potentially leading to a denial of service or arbitrary code
execution. |
| 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. |
| Axios is a promise based HTTP client for the browser and Node.js. From 0.19.0 to before 0.31.1 and 1.15.2, Axios contains prototype-pollution gadgets in request config processing. If another vulnerability in the same JavaScript process has already polluted Object.prototype.transformResponse, affected Axios versions may treat that inherited value as request configuration or as an option validator. Axios does not itself create the prototype pollution. Exploitability requires a separate prototype-pollution vulnerability or equivalent attacker control over Object.prototype before Axios creates a request. This vulnerability is fixed in 0.31.1 and 1.15.2. |
| 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. |
| A flaw was found in CRI-O's container-creation environment-variable handling
(`mergeEnvs` in `server/utils.go`, consumed by `setupContainerEnvironmentAndWorkdir` in
`server/container_create.go`). When a `CreateContainer` request supplies a `nil` CRI
`Envs` field, CRI-O falls back to using the target OCI image's `config.Env` entries
unfiltered, in contrast to the normal merge path, which validates each entry for a
`key=value` form before use. An OCI image whose `config.Env` contains an entry with no
`=` character (e.g. a bare `NOEQUALS` string) causes CRI-O to split that entry into a
single-element slice and then index its second element, which is out of range. This
triggers an unrecovered Go runtime panic in the `crio` daemon process, crashing it and
terminating the container-runtime service for all workloads on the node until it is
restarted. |
| 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. |
| 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. |
| A flaw was found in the maas-api and maas-controller ServiceAccounts within Red Hat OpenShift AI. These ServiceAccounts are granted cluster-wide permissions that exceed their operational requirements. An attacker who compromises the identity of these ServiceAccounts, either through a remote code execution vulnerability or by creating a malicious pod in the same namespace, could exploit these excessive permissions. This could lead to full cluster administrator privileges through the creation of new ClusterRoleBindings or the disclosure of sensitive information by accessing all secrets across the cluster. |
| 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 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. |
| 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. |
| 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 execution on the feature server in default configurations. An authenticated attacker can also achieve arbitrary code execution on the registry server by bypassing authorization checks during deserialization. This vulnerability can result in cross-tenant data access and lateral movement within the system. |
| 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. |
| 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. |
| 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. |
