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,...

Adversarial Robustness of Phishing Email Detection: A Comparative Study of TF-IDF + Logistic Regression and Fine-Tuned DistilBERT

Phishing emails remain one of the most persistent cybersecurity threats, and machine-learning classifiers are widely used to detect them. Most reported detection accuracies, however, are measured on clean, in-distribution test data rather than on emails deliberately altered to evade detection. Th...

Shared Vulnerabilities in Robustness-Optimized Defenses: One Breach Exposes the Family

Adversarial robustness optimization aims to preserve correct prediction under adversarial perturbations, and has produced substantial robustness gains through methods such as adversarial training and adversarial purification. However, we identify a new security risk: these gains can create shared...

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...

Traffic-Aware Randomized Smoothing for LLM-Based Network Intrusion Detection

Large language model LLM-based intrusion detection systems IDS are increasingly studied for security monitoring, yet their robustness against feasible traffic manipulation remains largely empirical. We present Traffic-Aware Randomized Smoothing TA-RS, a classifier-agnostic certified defense that...

Mechanistic Interpretability of LLM Jailbreaks Via Internal Attribution Graphs

Large language models LLMs exhibit remarkable capabilities but remain highly vulnerable to adversarial prompts and jailbreak attacks. Existing approaches primarily analyze these failures through input-output behaviors or attribution methods, offering limited insight into how adversarial...

When Agents Go Rogue: Activation-Based Detection of Malicious Behaviors in Multi-Agent Systems

While enabling effective collaboration on complex tasks, LLM-based Multi-Agent Systems MAS face critical security challenges due to vulnerabilities at the agent and interaction levels. Most existing MAS security defenses are built upon two core assumptions: semantically-explicit malicious attacks...

[SECURITY] Fedora 44 Update: mir-2.26.0-2.fc44

Mir is a Wayland display server toolkit for Linux systems, with a focus on efficiency, robust operation, and a well-defined driver model...

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...

Beyond Gradient-Based Attacks: Adversarial Robustness and Explainability Stability in Cybersecurity Classifiers

Adversarial attacks on cybersecurity classifiers pose a dual threat: degrading predictions and destabilising the SHAP-based explanations that security analysts rely on to understand and triage alerts. We extend our prior MLP conference study to Random Forest and XGBoost across four tabular securi...

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...

CITADEL: CSI-Based Jamming Detection and Open-Set Classification for IIoT Networks

Radio frequency jamming poses a critical threat to the availability of wireless Industrial Internet of Things IIoT networks. Existing detection and classification techniques are poorly suited to this setting: coarse signal-strength and cross-layer features lack information richness, while raw I/Q...

LLM Agent Safety, Multi-Turn Red-Teaming, Jailbreak Benchmarks, Adversarial Robustness, Safety-Critical Systems

Large language model LLM agents are increasingly proposed as supervisory components for safety-critical systems, yet their robustness under sustained, adaptive adversarial pressure remains poorly characterized. We present NRT-Bench, a benchmark for multi-turn red-teaming of LLM agents acting as...

ShellGames: Speculative LLM-Driven SSH Deception

Cyber deception and Moving Target Defense are promising strategies that aim to disrupt adversaries by increasing uncertainty. However, sustaining long-lived, credible interactive sessions with adversaries remains an open challenge. Large Language Models LLMs offer a promising path toward more...

FreeType Glyph Parallel Range Scanner for Font Parsing Stability Testing

This Python tool performs automated glyph parsing validation across a configurable range of glyph indices within a font file using FreeType bindings. It loads the target font, extracts metadata such as family name, style, and glyph count, then iteratively tests glyph loading operations using...

FreeType SHZ 2.14.3 Font Parsing Analyzer for Glyph Stability Assessment

This Python script performs basic robustness testing of font parsing behavior using the FreeType bindings by loading a target font and iterating through a large set of glyph indices. For each glyph, it attempts non-scaled and non-hinted glyph loading while collecting exceptions that may indicate...