AIX360

AI Explainability 360 v0.3.0 ✨ NEW: IBM Research has a new In-Context Explainability 360 ICX360 toolkit that extends explainability to LLMs , specifically in terms of the input given to the LLM. Please check it out! 🚀✨ The AI Explainability 360 toolkit is an open-source library that supports...

Membership Inference Attack on Private Evolution

Synthetic Data Generation SDG is an actively studied way to enable safe sharing of private data more broadly. Inference-only SDG methods, such as Private Evolution PE, are particularly appealing, as they can leverage black-box access to pre-trained foundation models to generate high quality data...

Multi-Class, Multi-Tier Network Intrusion Detection: A Comprehensive and Reproducible Benchmark

Machine learning ML and deep learning DL have dominated Intrusion Detection System IDS research in recent years. Unfortunately, many existing studies have produced inflated results and unreliable benchmarks due to critical oversights and mistakes in the ML and DL pipeline, from data collection an...

Are We Shooting Flies with Cannons? Trade-Off Analysis for AI-Based 5G Intrusion Detection

The increasing adoption of Artificial Intelligence AI in network intrusion detection raises the question of whether complex and computationally expensive models are justified for this task. In this work, we investigate the trade-off between detection performance and computational cost for intrusi...

[SECURITY] Fedora 44 Update: python-tablib-3.10.0-1.fc44

Tablib is a format-agnostic tabular dataset library, written in Python...

[SECURITY] Fedora 43 Update: python-tablib-3.10.0-1.fc43

Tablib is a format-agnostic tabular dataset library, written in Python...

Revisiting Data-Driven Dynamic Security Assessment with a Tabular Foundation Model

Data-driven pre-fault dynamic security assessment DSA rapidly evaluates the dynamic risk of credible contingencies on a power system using machine learning. Existing approaches face two limitations. First, they require a large labelled database for training, with a separate model trained, tuned,...

autogluon-extra (>=0.0.15b20201205, <=0.0.16b20210104), autogluon-tabular (>=0.0.15b20201205, <=0.0.16b20210104) +26 more potentially affected by CVE-2024-12216 via gluoncv (>=0.3.0, <=0.9.0)

gluoncv PYPI version =0.3.0, =0.0.15b20201205, =0.0.15b20201205, =0.0.15b20201205, =0.1.1, =0.1.0, =0.0.1, =0.0.20 - monk-cpu 0.0.1 - monk-cpu-test 0.0.11 - monk-cuda100 0.0.1 - monk-cuda100-test 0.0.1 - monk-cuda101 0.0.1 - monk-cuda101-test 0.0.1 - monk-cuda90 0.0.1 - monk-cuda90-test 0.0.1 -...

CVE-2026-24221

NVIDIA NVTabular contains a vulnerability where an attacker could cause improper deserialization of untrusted data. A successful exploit of this vulnerability might lead to code execution, data tampering and information disclosure...

PT-2026-45798

Name of the Vulnerable Software and Affected Versions NVIDIA NVTabular affected versions not specified Description Improper deserialization of untrusted data occurs when an application fails to sufficiently validate data before converting it from a serialized format back into an object. This issu...

autogluon (>=0.0.13b20200721, <=0.1.1b20210419), autogluon-core (>=0.0.15b20201014, <=0.0.15b20201023) +14 more potentially affected by CVE-2026-23528 via distributed (2023.5.0)

distributed PYPI version 2023.5.0 is affected by a known vulnerability. The following packages have a transitive dependency on distributed and may be impacted: - autogluon =0.0.13b20200721, =0.0.15b20201014, =0.0.15b20201024, =0.9.0, =0.9.0, =0.0.4.5, =1.0.0, =0.2.7, =0.3.0, =0.1.0, =2021.4.28rc1...

autogluon (>=0.3.2b20211216, <=0.5.3), autogluon-core (>=0.3.2b20211216, <=0.6.3b20230202) +19 more potentially affected by CVE-2026-23528 via distributed (2021.11.2)

distributed PYPI version 2021.11.2 is affected by a known vulnerability. The following packages have a transitive dependency on distributed and may be impacted: - autogluon =0.3.2b20211216, =0.3.2b20211216, =0.6.3b20230126, =0.4.3b20220616, =0.3.2b20211216, =0.3.2b20211216, =0.5.0b20220623,...

MiracleLinux 4 : java-1.7.0-openjdk-1.7.0.181-2.6.14.1.AXS4 (AXSA:2018-2999:02)

The remote MiracleLinux 4 host has packages installed that are affected by multiple vulnerabilities as referenced in the AXSA:2018-2999:02 advisory. OpenJDK: incorrect handling of Reference clones can lead to sandbox bypass Hotspot, 8192025 CVE-2018-2814 OpenJDK: unrestricted deserialization of...

CVE-2025-33214

NVIDIA NVTabular for Linux contains a vulnerability in the Workflow component, where a user could cause a deserialization issue. A successful exploit of this vulnerability might lead to code execution, denial of service, information disclosure, and data tampering...

SD-CGAN: Conditional Sinkhorn Divergence GAN for DDoS Anomaly Detection in IoT Networks

The increasing complexity of IoT edge networks presents significant challenges for anomaly detection, particularly in identifying sophisticated Denial-of-Service DoS attacks and zero-day exploits under highly dynamic and imbalanced traffic conditions. This paper proposes SD-CGAN, a Conditional...

MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection

High-quality data scarcity hinders malware detection, limiting ML performance. We introduce MalDataGen, an open-source modular framework for generating high-fidelity synthetic tabular data using modular deep learning models e.g., WGAN-GP, VQ-VAE. Evaluated via dual validation TR-TS/TS-TR, seven...

EUVD-2012-4133

Malware in sbrugna...

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection

The Internet of Things IoT, with its high degree of interconnectivity and limited computational resources, is particularly vulnerable to a wide range of cyber threats. Intrusion detection systems IDS have been extensively studied to enhance IoT security, and machine learning-based IDS ML-IDS show...

Foe for Fraud: Transferable Adversarial Attacks in Credit Card Fraud Detection

Credit card fraud detection CCFD is a critical application of Machine Learning ML in the financial sector, where accurately identifying fraudulent transactions is essential for mitigating financial losses. ML models have demonstrated their effectiveness in fraud detection task, in particular with...

Tab-MIA: a Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs

Large language models LLMs are increasingly trained on tabular data, which, unlike unstructured text, often contains personally identifiable information PII in a highly structured and explicit format. As a result, privacy risks arise, since sensitive records can be inadvertently retained by the...