PYSEC-2026-972 Core dump when loading TFLite models with quantization in TensorFlow

Impact Certain TFLite models that were created using TFLite model converter would crash when loaded in the TFLite interpreter. The culprit is that during quantization the scale of values could be greater than 1 but code was always assuming sub-unit scaling. Thus, since code was calling...

TACTIC-KG: Toward Small Agent Teams for Cyber Threat Intelligence Knowledge Graph Construction

Cyber Threat Intelligence CTI reports are predominantly unstructured, heterogeneous, and noisy, which limits their direct usability for automated analysis and reasoning. Cybersecurity Knowledge Graphs CSKGs provide a structured representation of adversarial entities, actions, and relations, but...

RustMizan: A Compilable, Contamination-Aware Benchmarking Framework for Rust Vulnerabilities

LLM agents are increasingly applied to vulnerability analysis, but existing benchmarks have not kept pace. They typically rely on small non-compilable snippets, focus on binary classification vulnerable or not, and do not account for the risk that publicly-released datasets are part of model...

Out-of-bounds Read

Overview onnx is an Open Neural Network Exchange Affected versions of this package are vulnerable to Out-of-bounds Read through convPoolShapeInferenceopset19 in onnx/defs/nn/old.cc. An attacker can trigger the flaw by supplying a malformed convolution or pooling model that reaches the...

EUVD-2025-210423

Picklescan before 0.0.33 fails to detect the numpy.f2py.crackfortran.getlincoef gadget in pickle reduce methods, allowing arbitrary code execution. Attackers can craft malicious pickle files that execute arbitrary Python code when loaded, bypassing Picklescan's safety checks and enabling...

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...

(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 ...

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...

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...

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...

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...