| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| PraisonAI is a multi-agent teams system. Prior to praisonai 4.6.58, the MCP HTTP Stream _validate_origin method accepts request_origin.startswith(allowed), so the attacker-controlled localhost.evil.example HTTP origin matches the localhost allowlist. Without an API key, a malicious webpage can submit tools/call requests to the local MCP server and execute exposed tools. This issue is fixed in version 4.6.58. |
| Analyzing a PDF with a deeply nested or cyclic table of contents can cause a StackOverflowError in the ingestion thread.
Spring AI 2.0.0
Spring AI 1.1.0 - 1.1.8
Spring AI 1.0.0 - 1.0.9 |
| A security vulnerability has been detected in macrozheng mall up to 1.0.3. Affected is the function OmsCartItemServiceImpl.updateQuantity of the file /cart/update/quantity. The manipulation of the argument quantity leads to business logic errors. The attack may be initiated remotely. The vendor deleted the GitHub issue for this vulnerability without any explanation. |
| Ech0 through 4.2.1 contains a server-side request forgery vulnerability in the validateWebhookURL function (webhook_setting_service.go), which only validates literal IP addresses via net.ParseIP() and fails to reject hostnames that DNS-resolve to private or internal IPs (e.g., 169.254.169.254.nip.io). An attacker with admin privileges can create a webhook with such a hostname to bypass validation and cause the server to make requests to internal services, cloud metadata endpoints, and private network resources. The issue is fixed in 4.4.3. |
| Ech0 before 4.4.3 fails to enforce administrator authorization on dashboard log endpoints, allowing any authenticated user to access system logs. Attackers with valid user sessions can query GET /api/system/logs and subscribe to SSE and WebSocket log streams to retrieve sensitive operational data including file paths, stack traces, and internal URLs. |
| 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. |
| The All-in-One WP Migration and Backup plugin for WordPress is vulnerable to SQL Injection via archive restore functionality in all versions up to, and including, 7.109 due to insufficient escaping on the user supplied parameter and lack of sufficient preparation on the existing SQL query. This makes it possible for unauthenticated attackers to append additional SQL queries into already existing queries that can be used to extract sensitive information from the database. This can be leveraged to obtain the ai1wm_secret_key when a site administrator performs an archive restore and achieve remote code execution once able to leverage the ai1wm_secret_key value. |
| 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. |
| 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. |
| 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 and configurations, and inject arbitrary data into the service, potentially disrupting tenant operations. |
| 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 execute untrusted remote code, leading to arbitrary code execution within the cluster. |
| 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 access to sensitive information, including credentials from cloud metadata services, Kubernetes API, internal MinIO, and other internal network endpoints. Additionally, it enables local file reads of critical data such as service account tokens and pod secrets. |
| A producer who can publish to a JMS destination consumed by any Spring Integration JMS inbound component can set String JMS properties named replyChannel, errorChannel, or json__TypeId__ which are copied verbatim into the Spring Integration MessageHeaders.
Spring Integration 7.1.0
Spring Integration 7.0.0 - 7.0.5
Spring Integration 6.5.0 - 6.5.10
Spring Integration 6.4.0 - 6.4.12
Spring Integration 5.5.21 and earlier |
| A flaw was found in the MaaS API. This vulnerability allows any pod within the cluster to bypass the Kuadrant AuthPolicy gateway by forging HTTP headers, specifically `X-MaaS-Username` and `X-MaaS-Group`, which are trusted verbatim. This lack of first-party authentication enables an attacker to gain unauthorized access and escalate privileges. The concrete consequences include the ability to mint Kubernetes ServiceAccount tokens in other tenants' namespaces, revoke API keys, and exfiltrate sensitive model access configuration. |
| 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. |
| A vulnerability was found in liketrek TREK up to 3.0.22. Impacted is the function loginUser of the file server/src/services/authService.ts of the component Pre-2FA mfa_token Handler. The manipulation results in improper authentication. The attack may be performed from remote. Upgrading to version 3.1.0 is recommended to address this issue. Upgrading the affected component is recommended. |
