Stack-based buffer overflow in Windows Netlogon allows an unauthorized attacker to execute code over a network.
Use after free in Windows Hyper-V allows an unauthorized attacker to elevate privileges locally.
Exposure of sensitive information to an unauthorized actor in Azure Entra ID allows an unauthorized attacker to perform spoofing over a network.
The Java Key Vault Keys library in the Azure SDK for Java contains an issue in the local cryptographic verification path where authentication tag comparison was implemented incorrectly. In affected applications that use the vulnerable local cryptography path, specially crafted encrypted input may bypass integrity verification checks. Operations delegated to the Key Vault service are not affected. The issue is addressed in version 4.10.6.
The mem0 v1.0.0 server lacks authentication and authorization controls for its memory reset functionality accessible via the DELETE /memories endpoint. An unauthenticated attacker can send a DELETE request that triggers a reset operation, leading to the execution of a DROP TABLE SQL statement. This results in the deletion of the entire memory database table, causing catastrophic data loss and a complete denial of service for all users of the service.
The mamba language model framework thru 2.2.6 is vulnerable to insecure deserialization (CWE-502) when loading pre-trained models from HuggingFace Hub. The MambaLMHeadModel.from_pretrained() method uses torch.load() to load the pytorch_model.bin weight file without enabling 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 publishing a malicious model repository on HuggingFace H
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
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
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 eng
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.
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 send
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 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 w
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 f
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.
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.