CVE Database

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization (CWE-502) in its model serving component. When starting a model server with the ludwig serve command, the framework loads model weight files using torch.load() without enabling the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing a maliciously crafted PyTorch model file, leading to arbitrary code execution on the system hosting the Ludwig model server.

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization (CWE-502) through its predict() method. When a user provides a dataset file path to the predict() method, the framework automatically determines the file format. If the file is a pickle (.pkl) file, it is loaded using pandas.read_pickle() without any validation or security restrictions. This allows the deserialization of arbitrary Python objects via the unsafe pickle module. A remote attacker can exploit this by providing a maliciously crafted pickle file, leading to arbitrary code execution on the system running the Ludwig prediction.

The llm CLI tool thru 0.27.1 contains a critical code injection vulnerability via its --functions command-line argument. This argument is intended to allow users to provide custom Python function definitions. However, the tool directly executes the provided code using the unsafe exec() function without any sanitization, sandboxing, or security restrictions. An attacker can exploit this by crafting a malicious llm command with arbitrary Python code in the --functions argument and using social engineering to trick a victim into running it. This leads to arbitrary code execution on the victim's system, potentially granting the attacker full control.

The imgaug library thru 0.4.0 contains an insecure deserialization vulnerability in its BackgroundAugmenter class within the multicore.py module. The class uses Python's pickle module to deserialize data received via a multiprocessing queue in the _augment_images_worker() method without any safety checks. An attacker who can influence the data placed into this queue (e.g., through social engineering, malicious input scripts, or a compromised shared queue) can provide a malicious pickle payload. When deserialized, this payload can execute arbitrary code in the context of the worker process, leading to remote or local code execution depending on the deployment scenario.

Horovod thru 0.28.1 contains an insecure deserialization vulnerability (CWE-502) in its KVStore HTTP server component. The KVStore server, used for distributed task coordination, lacks authentication and authorization controls, allowing any remote attacker to write arbitrary data via HTTP PUT requests. When a Horovod worker reads data from the KVStore (via HTTP GET), it deserializes the data using cloudpickle.loads() without verifying its source or integrity. An attacker can exploit this by sending a malicious pickle payload to the server before the legitimate data is written, causing the victim worker to deserialize and execute arbitrary code, leading to remote code execution.

Guardrails AI thru 0.6.7 contains a code injection vulnerability (CWE-94) in its Hub package installation mechanism. When installing validator packages via guardrails hub install, the system retrieves a manifest from the Guardrails Hub and dynamically executes a script specified in the post_install field. The script path is constructed from untrusted manifest data and executed without proper validation or sanitization, allowing remote code execution. An attacker who can publish malicious packages to the Hub can inject arbitrary code that will be executed on any system where a victim installs the malicious package.

Cognee thru v0.4.0 contains a critical remote code execution vulnerability in its notebook cell execution API endpoint. The endpoint is designed to execute arbitrary Python code provided by the user, but it does so using the unsafe exec() function without any sandboxing, validation, or security controls. An attacker can exploit this by sending a specially crafted POST request containing malicious Python code to the execution endpoint. This leads to arbitrary code execution on the Cognee server with the privileges of the server process, allowing complete compromise of the system.

The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains a command-line argument injection vulnerability in its Kubeflow component (robustness_evaluation_fgsm_pytorch.py). The script uses the unsafe eval() function to parse string values provided via the --clip_values and --input_shape command-line arguments. This allows an attacker to inject arbitrary Python code into these arguments, which will be executed when eval() is called. The vulnerability can be exploited remotely if an attacker can control these arguments (e.g., through pipeline configuration or automated scripts), leading to arbitrary code execution on the system running the ART evaluation.

The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains an insecure deserialization vulnerability (CWE-502) in its Kubeflow component's model loading functionality. When loading model weights from a file (e.g., model.pt) during robustness evaluation, the code uses torch.load() without the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the Pickle module. An attacker can exploit this by uploading a maliciously crafted model file to an object storage location referenced by the pipeline, or by controlling the model_id parameter to point to such a file. When the pipeline loads the model, the malicious payload is executed, leading to remote code execution.

Insufficient ownership check in `clientarea.php` allows an authenticated client area user to submit requests using another user’s `addonId` without any ownership validation leading to unauthorized access to the victim's account.

A missing authorization vulnerability in Fortinet FortiSandbox 5.0.0 through 5.0.1, FortiSandbox 4.4.0 through 4.4.8, FortiSandbox Cloud 5.0.2 through 5.0.5, FortiSandbox PaaS 23.4 all versions, FortiSandbox PaaS 23.3 all versions, FortiSandbox PaaS 23.1 all versions, FortiSandbox PaaS 22.2 all versions, FortiSandbox PaaS 22.1 all versions, FortiSandbox PaaS 21.4 all versions, FortiSandbox PaaS 21.3 all versions, FortiSandbox PaaS 5.0.0 through 5.0.1, FortiSandbox PaaS 4.4.5 through 4.4.8 may allow an unauthenticated attacker to execute unauthorized code or commands via HTTP requests.

JunoClaw is an agentic AI platform built on Juno Network. Prior to 0.x.y-security-1, every MCP write tool (send_tokens, execute_contract, instantiate_contract, upload_wasm, ibc_transfer, etc.) accepted 'mnemonic: string' as an explicit tool-call parameter. The BIP-39 seed was consequently embedded in the LLM tool-call JSON, exposing it to any transport, log, or telemetry surface in the path between the LLM provider and the MCP process. This vulnerability is fixed in 0.x.y-security-1.

An issue in Open Source Kubectl MCP Server v1.1.1 allows attackers to execute arbitrary code on a victim system via user interaction with a crafted HTML page.

Improper Authorization vulnerability when multiple method constraints define an HTTP method for the same extension in Apache Tomcat. This issue affects Apache Tomcat: from 11.0.0-M1 through 11.0.21, from 10.1.0-M1 through 10.1.54, from 9.0.0.M1 through 9.0.117, from 8.5.0 through 8.5.100, from 7.0.0 through 7.0.109. Users are recommended to upgrade to version 11.0.22, 10.1.55 or 9.0.118 which fix the issue.

DEPRECATED: Authentication Bypass Issues vulnerability in digest authentication in Apache Tomcat. This issue affects Apache Tomcat: from 11.0.0-M1 through 11.0.21, from 10.1.0-M1 through 10.1.54, from 9.0.0.M1 through 9.0.117, from 8.5.0 through 8.5.100, from before 7.0.0. Older unsupported versions any also be affect Users are recommended to upgrade to version 11.0.22, 10.1.55 or 9.0.118 which fix the issue.

Improper Input Validation vulnerability in Apache Tomcat. This issue affects Apache Tomcat: from 11.0.0-M1 through 11.0.21, from 10.1.0-M1 through 10.1.54, from 9.0.0.M1 through 9.0.117, from 10.0.0-M1 through 10.0.27. Older, end of support versions may also be affected. Users are recommended to upgrade to version [FIXED_VERSION], which fixes the issue.

Improper Neutralization of Special Elements used in an SQL Command vulnerability allows SQL Injection via graph container parameter. This issue affects Pandora FMS: from 777 through 800

The Adversarial Robustness Toolbox (ART) thru 1.20.1 contains a remote code execution vulnerability in its Kubeflow component. The robustness evaluation function for PyTorch models uses the unsafe eval() function to dynamically evaluate user-supplied strings for the LossFn and Optimizer parameters without any sanitization or security restrictions. An attacker can exploit this by providing a specially crafted string that contains arbitrary Python code, which will be executed when eval() is called, leading to complete compromise of the system running the ART evaluation.

The TinyZero project thru commit 6652a63c57fa7e5ccde3fc9c598c7176ff15b839 (2025-58-24) contains a critical command injection vulnerability (CWE-78) in its HDFS file operation utilities. The vulnerability arises from the unsafe construction and execution of shell commands via os.system() without proper input sanitization or escaping. User-controlled input (such as file paths) is directly interpolated into shell command strings using f-strings within the _copy() function. An attacker can inject arbitrary OS commands by supplying a specially crafted path parameter through the Hydra configuration framework. This leads to remote code execution with the privileges of the user running the TinyZero training process.

PySyft (Syft Datasite/Server) versions 0.9.5 and earlier are vulnerable to remote code execution due to insufficient validation and sandboxing of user-submitted code. The system allows low-privileged users to submit Python functions (via @sy.syft_function()) for remote execution on the server. While a code approval mechanism exists, the submitted code undergoes no security checks for dangerous operations (e.g., file access, command execution). Once approved, the code is executed within the server process using exec() and eval() functions without proper isolation. A remote attacker can leverage this to execute arbitrary Python code on the server, leading to complete compromise of the server environment.

The _load_model() function in the neural_magic_training.py script of the optimate project in commit a6d302f912b481c94370811af6b11402f51d377f (2024-07-21) allows arbitrary code execution. When a user supplies a directory path via the --model command-line argument, the function reads a module.py file from that directory and executes its contents directly using Python's exec() function. This design does not validate or sanitize the file's content, allowing an attacker who controls the input directory to execute arbitrary Python code in the context of the process running the script.

The nexent v1.7.5.2 backend service contains an unauthorized arbitrary storage file deletion vulnerability in its file management API. The DELETE /storage/{object_name:path} endpoint lacks authentication, authorization, and input validation mechanisms. Unauthenticated remote attackers can send crafted requests with a user-controlled object_name path parameter to delete arbitrary files from the underlying MinIO storage system. Successful exploitation leads to data loss and denial of service.

The nexent v1.7.5.2 backend service contains an unauthorized arbitrary file deletion vulnerability in its ElasticSearch service interface. The DELETE /{index_name}/documents endpoint lacks proper authentication and authorization controls and does not validate the user-supplied path_or_url parameter. This allows unauthenticated remote attackers to send crafted requests that trigger the deletion of arbitrary documents from ElasticSearch indices and corresponding files from the MinIO storage system. Successful exploitation leads to data destruction and denial of service.

The torch-checkpoint-shrink.py script in the ml-engineering project in commit 0099885db36a8f06556efe1faf552518852cb1e0 (2025-20-27) contains an insecure deserialization vulnerability (CWE-502). The script uses torch.load() to process PyTorch checkpoint files (.pt) without enabling the security-restrictive weights_only=True parameter. This oversight allows the deserialization of arbitrary Python objects via the pickle module. A remote attacker can exploit this by providing a maliciously crafted checkpoint file, leading to arbitrary code execution in the context of the user running the script.

Insecure Default Initialization of Resource vulnerability allows Authentication Bypass via API access. This issue affects Pandora FMS: from 777 through 800

Sandbox escape in the Profile Backup component. This vulnerability was fixed in Firefox 150.0.3, Firefox ESR 115.36, Firefox ESR 140.11, and Thunderbird 140.11.

External control of a file name in Ivanti Xtraction before version 2026.2 allows a remote authenticated attacker to read sensitive files and write arbitrary HTML files to a web directory, leading to information disclosure and possible client-side attacks.

sealed-env is a cross-stack, zero-trust secret management library for Node.js and Java/Spring Boot. In sealed-env enterprise mode, versions 0.1.0-alpha.1 through 0.1.0-alpha.3 embedded the operator's literal TOTP secret in the JWS payload of every minted unseal token. JWS payload is base64-encoded JSON, NOT encrypted. Any party who could observe a minted token (CI build logs, container env dumps, kubectl describe pod, Sentry/Rollbar stack traces, log aggregators) could decode the payload and extract the TOTP secret in plaintext. This vulnerability is fixed in 0.1.0-alpha.4.

A vulnerability has been identified in ROS# (All versions < V2.2.2). Affected versions contain a path traversal vulnerability because user input is not properly sanitized. This could allow a remote attacker to access arbitrary files on the device.

Affected devices do not properly validate and sanitize Technology Object (TO) name rendered on the "Motion Control Diagnostics" page of the web interface. This could allow an authenticated attacker who is authorized to download a TIA project into the product, to inject malicious scripts into the page. If a benign user with appropriate rights accesses the "Motion Control Diagnostics" parameters page, the malicious code would be executed in the scope of their web session.