craw

CRAW: Codec Robust Audio Watermarking Source code for CRAW , a robust audio watermarking method built on STFT-magnitude-domain embedding. An encoder embeds a binary message into an audio clip's magnitude spectrogram phase is preserved from the clean signal; a decoder recovers the message under a...

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...

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...

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...

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...

Q-MALDetect: Quantum Image-Based Malware Detection and Adversarial Robustness Analysis

Quantum machine-learning QML models are increasingly considered for security applications, yet their adversarial robustness in malware analysis remains insufficiently understood. This paper presents Q-MALDetect, a hybrid convolutional and variational-quantum detector for byte-image representation...

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...

stegowiper

stegoWiper: Un ataque activo potente y flexible para interrumpir el stegomalware Durante los últimos 10 años, muchos grupos de amenazas han empleado stegomalware u otras técnicas basadas en esteganografía para atacar organizaciones de todos los sectores y en todas las regiones del mundo. Algunos...

deep-pwning

Deep-pwning è un framework leggero per sperimentare con modelli di machine learning, con l'obiettivo di valutarne la robustezza contro un avversario motivato. Si noti che deep-pwning nel suo stato attuale è lontano dall'essere maturo o completo. È pensato per essere sperimentato, ampliato ed este...

Adaptive Model Inversion Attacks Generalize a Privacy-Robustness Tradeoff

In this paper, we show that standard evaluations of high-resolution Model Inversion Attacks MIAs significantly underestimate training-data privacy leakage. State-of-the-art privacy defenses, standard training techniques such as MixUp and Adversarial Training, and undefended models all leak traini...

COPEX: Benchmarking LLM Robustness to Adversarial Context across Model Context Protocol Layers

Large language models increasingly mediate tool use in Model Context Protocol MCP systems, where adversarial influence may enter through user instructions, tool schemas, tool outputs, or protocol messages. Existing benchmarks often evaluate deployed agents, conflating model susceptibility with...

APTInvestBench: Evaluating Autonomous APT Investigation under Varying Telemetry

Large language model LLM agents could help security operations centers SOCs investigate advanced persistent threats APTs by turning weak leads into evidence for intrusion scoping and response. Yet success under one telemetry setting does not establish robustness to changes in log collection,...

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...

Towards Understanding LLM-Based Log Anomaly Detection: An Empirical Study of Performance, Efficiency, and Robustness

Large language models LLMs have demonstrated promising performance in log anomaly detection, yet how their adaptation strategies, architectures, and deployment configurations affect detection effectiveness remains insufficiently understood. To investigate these factors, we conduct a systematic...

SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes

Autonomous large language model LLM agents are moving rapidly into high-stakes domains, yet existing agentic-AI security studies remain largely domain-agnostic and overlook the distinctive, high-consequence attack surface such settings create. We examine this gap through financial trading agents,...

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...

CVE-2026-80900

In the Linux kernel, the following vulnerability has been resolved: ASoC: SDCA: Make UMP message size check more robust If message offset was larger than the buffer length the size check will pass incorrectly. Refactor the check such that it is more robust to invalid sizes...