Sec5GLoc

Sec5GLoc: Securing 5G Indoor Localization via Adversary-Resilient Deep Learning Architecture Our work addresses security and privacy challenges in 5G indoor localization by proposing an adversary-resilient deep learning architecture. Sec5GLoc combines Channel Impulse Response CIR fingerprinting...

robustness

robustness package Install via pip: pip install robustness Read the docs: https://robustness.readthedocs.io/en/latest/index.html robustness is a package we students in the MadryLab created to make training, evaluating, and exploring neural networks flexible and easy. We use it in almost all of ou...

robustbench

RobustBench: a standardized adversarial robustness benchmark Francesco Croce University of Tübingen, Maksym Andriushchenko EPFL, Vikash Sehwag Princeton University, Edoardo Debenedetti EPFL, Nicolas Flammarion EPFL, Mung Chiang Purdue University, Prateek Mittal Princeton University, Matthias Hein...

adversarial-robustness-toolbox

Adversarial Robustness Toolbox ART v1.20 中文README请按此处 Fallback image description Adversarial Robustness Toolbox ART is a Python library for Machine Learning Security. ART is hosted by the Linux Foundation AI & Data Foundation LF AI & Data. ART provides tools that enable developers and researchers...

QuasarNix

QuasarNix: Adversarially Robust Living-off-the-Land Reverse-Shell Detection Dmitrijs Trizna · Luca Demetrio · Battista Biggio · Fabio Roli Overview Linux living-off-the-land LOTL reverse shells abuse legitimate binaries bash, python, nc, … to establish covert outbound connections, making...

StealthRL

StealthRL: Reinforcement Learning Paraphrase Attacks for Multi-Detector Evasion of AI-Text Detectors Paper arXiv Demo Model Hugging Face Benchmark Dataset Hugging Face Abstract AI-text detectors are increasingly used in high-stakes settings, yet their robustness to meaning-preserving adversarial...

LogiC-Diff: Embedding Security Properties into AI-Enabled Cyber-Physical Systems

AI-enabled Cyber-Physical Systems CPS are highly vulnerable to adversarial and anomalous inputs, where small perturbations can induce cascading errors and unsafe control actions. Existing approaches, such as rule-based filtering, training-time regularization, or diffusion-based reconstruction,...

Breaking Windows Malware Detection: A Comprehensive Evaluation of Problem-Space Adversarial Robustness

Problem-space evasion attacks have exposed critical weaknesses in machine learning-based malware detectors; yet, their evaluation remains fragmented across models, datasets, and attack methodologies, often neglecting domain-specific requirements such as executability and functionality preservatio...

Windows Malware Detector As a Compound AI System: Trade-Offs in Accuracy, Efficiency, and Adversarial Robustness

Industrial Windows malware detectors are commonly described as Compound AI Systems composed of multiple heterogeneous components, including rule-based mechanisms as well as machine-learning-based static and dynamic analyses. However, due to industrial secrecy and limited public disclosure, the...

Towards a Resilience-Theoretic Foundation for Adversarial Robustness in Industrial Control System Anomaly Detection

Anomaly-based intrusion detection systems in industrial control systems ICS and operational technology OT environments are increasingly required to meet formal resilience criteria: absorbed adversarial disturbances, graceful degradation under sustained attack, and certified system-level guarantee...

Enhancing Web Application Firewalls with Machine Learning for SQL Injection Detection

Detecting SQL Injection SQLi attacks ranks among the most critical challenges in web application security. This research conducted a systematic literature review to identify the research gaps in this domain and responsively designed and optimised a DistilBERT-Stacked Ensemble pipeline to improve...

Chameleon: Robust Defense against Tor Website Fingerprinting Via Many-To-Many Traffic Morphing

Website fingerprinting WF attacks can infer users' browsing activities from encrypted Tor traffic by exploiting side-channel features. Although many WF defenses have been proposed, we find that most existing defenses create learnable web trace mapping features. We further show that robustness...

Adversarial Robustness in Smishing Detection: A Comparative Analysis of Adversarial Fragility in Classical Vs. Transformer-Based Detection Systems

Smishing detection systems are commonly trained and evaluated on clean, monolingual text. In low-resource settings, however, attackers frequently circumvent these systems through character obfuscation, cross-lingual code-switching, and structural perturbation. This study evaluates adversarial...

EXE-Bench: Ranking the Tradeoffs of AI-Based Windows Malware Detectors for Real-World Usability

Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since existing evaluations i differ in terms of data used for both training and testing; ii do not consider temporal analysis to showcase whether models...

adversarial-robustness-toolbox (1.2.0), aristote-malo (1.9.5) +79 more potentially affected by CVE-2026-59205 via pillow (7.0.0)

pillow PYPI version 7.0.0 is affected by a known vulnerability. The following packages have a transitive dependency on pillow and may be impacted: - adversarial-robustness-toolbox 1.2.0 - aristote-malo 1.9.5 - auto-annotate =1.0.0, =1.0.2, =0.1.0.dev31, =1.0.1, =0.2.3, =0.0.1.25, =0.2.1, =0.0.2,...

adversarial-robustness-toolbox (1.2.0), aristote-malo (1.9.5) +79 more potentially affected by CVE-2026-55798 via pillow (7.0.0)

pillow PYPI version 7.0.0 is affected by a known vulnerability. The following packages have a transitive dependency on pillow and may be impacted: - adversarial-robustness-toolbox 1.2.0 - aristote-malo 1.9.5 - auto-annotate =1.0.0, =1.0.2, =0.1.0.dev31, =1.0.1, =0.2.3, =0.0.1.25, =0.2.1, =0.0.2,...

adversarial-robustness-toolbox (1.2.0), aristote-malo (1.9.5) +79 more potentially affected by CVE-2026-54058 via pillow (7.0.0)

pillow PYPI version 7.0.0 is affected by a known vulnerability. The following packages have a transitive dependency on pillow and may be impacted: - adversarial-robustness-toolbox 1.2.0 - aristote-malo 1.9.5 - auto-annotate =1.0.0, =1.0.2, =0.1.0.dev31, =1.0.1, =0.2.3, =0.0.1.25, =0.2.1, =0.0.2,...

Boundary-Seeking GAN-Augmented TabTransformer for Adversarially Robust Intrusion Detection

Machine learning-based intrusion detection systems IDSs often suffer from class imbalance and vulnerability to adversarial attacks, leading to degraded detection performance and reduced robustness. This study proposes a TabTransformer framework augmented by the Boundary-Seeking Generative...

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 ...

Explainability-Aware Frustum Attack: Exposing Structural Vulnerabilities in LiDAR-Based 3D Object Detectors

The structural vulnerabilities of point cloud-based 3D object detectors remain poorly understood. Prior work has studied adversarial robustness primarily on isolated 3D object models, while recent LiDAR spoofing attacks target richer and more realistic driving scenes but focus mainly on physical...