This paper introduces a novel, comprehensive dataset that logs various cheating activities, including difficult-to-detect network flow disruption cheats, for the purpose of developing robust detection…

BioMoTouch is a multi-modal touch authentication framework that jointly models physiological contact structures (from capacitive screens) and behavioral motion dynamics (from inertial sensors) to achi…

The paper introduces and analyzes cross-session stored prompt injection, demonstrating that persistent system state transforms prompt injection from a temporary model-level threat into a long-lived, s…

The paper introduces ARCANE, a Bayesian network framework for cross-campaign cyber attribution, finding that while aggregating telemetry improves identification, structural feature limitations prevent…

The paper introduces CyBiasBench, a comprehensive benchmark that quantifies the inherent, agent-specific bias in LLM agents' attack selection patterns in cybersecurity scenarios.

The paper introduces SkillVetBench, a novel two-stage benchmark that effectively detects and verifies malicious behavior in open agentic skill ecosystems, significantly outperforming existing static a…

The paper introduces SkillVetBench, a novel two-stage benchmark that effectively detects and verifies malicious behavior hidden within open agentic skills, significantly outperforming static and seman…

The paper proposes WARD, a robust and efficient defense model that secures web agents against prompt injection attacks embedded in web content, achieving high recall and low false positives even again…

The paper introduces an AI red teaming agent that drastically reduces the time and effort required for security testing by allowing operators to define complex attack goals using natural language, com…

The paper introduces FP-Agent, a classifier that demonstrates that while browser fingerprints are poor discriminators for AI browsing agents, behavioral fingerprints (like typing and scrolling pattern…

The paper introduces Persona-Conditioned Adversarial Prompting (PCAP), a method that significantly improves LLM red-teaming by simulating diverse attacker personas, leading to the discovery of more co…

The paper introduces BadSkill, a novel backdoor attack formulation that targets third-party agent skills by poisoning the embedded model artifacts, achieving high attack success rates across various m…

The paper introduces SkillSafetyBench, a comprehensive benchmark demonstrating that agent safety failures often stem from adversarial influences within reusable skills and execution environments, rath…

The paper introduces VRSafe, a novel virtual QWERTY keyboard designed to significantly mitigate keystroke inference attacks in virtual reality by introducing false positive keystrokes and incorporatin…

The paper introduces Persona-Conditioned Adversarial Prompting (PCAP), a novel framework that significantly enhances the discovery of jailbreaks by conditioning adversarial search on multiple attacker…

A-Live is a passive liveness detection framework that uses subtle neuromuscular micro-motion signatures captured by commodity IMU sensors to distinguish human users from non-human agents with high acc…

The paper proposes using GUI agents, both as objective evaluators and subjective playtesters, to significantly improve the generation of playable games from prompts, demonstrating a 66.8% rubric pass-…

The paper introduces NASimJax, a GPU-accelerated framework that significantly speeds up network simulation for reinforcement learning, enabling large-scale, realistic training for penetration testing.

The paper introduces SEED, a large-scale benchmark dataset for tracing sequential deepfake facial edits, and proposes FAITH, a frequency-aware Transformer model that effectively detects and orders the…

The paper introduces a framework to quantitatively measure evolving agent behaviors (traits) by analyzing changes in their configuration text files, achieving high accuracy in classifying behavioral s…