CVE-2025-71342

picklescan before 0.0.30 fails to detect malicious pickle files using idlelib.run.Executive.runcode in reduce methods. Attackers can embed undetected code in pickle files that executes during pickle.load, enabling remote code execution in PyTorch models and supply chain attacks...

CVE-2025-71342 picklescan - Undetected Remote Code Execution via idlelib.run.Executive.runcode

picklescan before 0.0.30 fails to detect malicious pickle files using idlelib.run.Executive.runcode in reduce methods. Attackers can embed undetected code in pickle files that executes during pickle.load, enabling remote code execution in PyTorch models and supply chain attacks...

Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities

The increasing complexity and frequency of software vulnerabilities demand efficient methods to analyze and prioritize threats. Traditional approaches often fail to process the vast amount of unstructured textual data effectively, highlighting the need for advanced solutions. This study leverages...

Missing Authorization

Overview Affected versions of this package are vulnerable to Missing Authorization via the label process. An attacker can obtain sensitive information by accessing labels associated with private organizations without proper authorization. Remediation Upgrade gitea.dev/models/auth to version 1.26....

CONTRA: Red-Teaming Configurations of Personalizable Agents

Recent tools such as OpenClaw have extended the capabilities of LLM-based agents from simple dialog-based systems to fully autonomous agents. These systems allow personalization of the agent through modifiable internal files and the installation of skills. While this enables deployment in a wide...

CVE-2026-12480

Keras versions up to and including 3.13.2 are vulnerable to an arbitrary HDF5 file read due to an incomplete fix for CVE-2026-1669. The vulnerability resides in the H5IOStore.verifydataset and fileeditor.py methods, which fail to check the dataset.isvirtual property of HDF5 datasets. This allows ...

Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks

Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things IIoT networks due to their suitability for resource-constrained edge deployment. Most reported results evaluate these models only within their training network, leaving behavior ...

Generative AI and Federated Learning for Intrusion Detection Systems: A Survey

Intrusion Detection Systems IDSs are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical, Internet of Things IoT, enterprise, and distributed network environments. However, developing reliable IDS models remains challenging because attack behavio...

Important: Red Hat Security Advisory: Red Hat Enterprise Linux AI 3.4.1 enhancement update

Updated Red Hat Enterprise Linux AI 3.4.1 container images are now available. Red Hat® Enterprise Linux® AI is a foundation model platform to seamlessly develop, test, and run Granite family large language models LLMs for enterprise applications. This update provides the latest Red Hat Enterprise...

Important: Red Hat Security Advisory: Red Hat Enterprise Linux AI 3.4.1 enhancement update

Updated Red Hat Enterprise Linux AI 3.4.1 container disk images are now available. Red Hat® Enterprise Linux® AI is a foundation model platform to seamlessly develop, test, and run Granite family large language models LLMs for enterprise applications. This update provides the latest Red Hat...

An Empirical Study of Security Calibration in Large Language Models for Code

Large Language Models LLMs are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated code is insecure? This property, known as calibration, measures whether a model's confidence aligns with the true...

(A)I Sees What You Don't: Exploiting New Attack Surfaces in Third-Party Mobile Agents

Third-party mobile agents powered by Vision-Language Models VLMs have emerged as a promising paradigm for automating smartphone interactions. These agents act as high-privilege decision-makers, perceiving device states through screenshots and executing actions via VLM reasoning, transforming how ...

MAL-2026-6648 Malicious code in @shopbop/api-models (npm)

--- -= Per source details. Do not edit below this line.=- Source: ghsa-malware b1e5f6c38f5d8b7befd57a2236e32bc2bc940467d300734af69b97f0b219cf2e Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be...

Malicious Package

Overview @shopbop/api-models is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization and this package...

PYSEC-2026-406 mamba language model framework vulnerable to insecure deserialization when loading pre-trained models from HuggingFace Hub

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.frompretrained method uses torch.load to load the pytorchmodel.bin weight file without enabling the security-restrictive...

mamba language model framework vulnerable to insecure deserialization when loading pre-trained models from HuggingFace Hub

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.frompretrained method uses torch.load to load the pytorchmodel.bin weight file without enabling the security-restrictive...

MLFlow Path Traversal Vulnerability

A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information...

PYSEC-2026-422 MLFlow Path Traversal Vulnerability

A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information...

PYSEC-2026-417 Remote Code Execution due to Full Controled File Write in mlflow

The mlflow web server includes tools for tracking experiments, packaging code into reproducible runs, and sharing and deploying models. As this vulnerability allows to write / overwrite any file on the file system, it gives a lot of ways to archive code execution like overwriting /home//.bashrc. ...

PYSEC-2026-514 Rasa Allows Remote Code Execution via Remote Model Loading

Vulnerability A vulnerability has been identified in Rasa Pro and Rasa Open Source that enables an attacker who has the ability to load a maliciously crafted model remotely into a Rasa instance to achieve Remote Code Execution. The prerequisites for this are: - The HTTP API must be enabled on the...