The paper analyzes the escalating security and safety threats posed by generative AI systems as they transition from merely generating content to executing real-world actions via tools and agents, fin…

This study proposes a negotiation framework, using composite indices (RBTI and CATI), to explain how youth navigate competing privacy pressures when using smart voice assistants, finding that high usa…

The paper addresses the lack of user understanding regarding the actions and residual effects of advanced computer-use agents by proposing AgentTrace, a traceability framework for visualizing agent be…

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 introduces STRIKE, a multi-dimensional structured taxonomy designed to provide a comprehensive and unified framework for classifying the rapidly evolving complexity of modern cybercrimes.

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by GenAI, moving beyond traditional react…

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by Generative AI, moving beyond tradition…

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

The paper introduces the concept of 'authenticity debt'—the institutional liability from deploying unverified AI content—and proposes a layered reference architecture combining cryptographic provenanc…

This study investigates how humans detect synthetic speech in real-world contexts, finding that while overt detection failed for fully synthetic speech, participants still implicitly discriminated utt…

This paper provides a unified taxonomy and controlled empirical evaluation of jailbreak attacks and defenses for Large Audio Language Models (LALMs), demonstrating that safety evaluation must consider…

The study compared the cybersecurity risk assessment capabilities of five popular large language models (LLMs) against human experts, finding that LLMs consistently underestimated risks and require ma…

The paper argues that deepfake detection research is misaligned because it focuses on historical threats (public-figure face-swaps) while ignoring the dominant, emerging harms like NCII, voice-cloning…

BraveGuard is a self-evolving defense framework that significantly improves the safety monitoring of computer-use agents by generating guard model supervision from open-world threat discovery and real…

BraveGuard is a self-evolving defense framework that improves the safety of computer-use agents by training guard models on open-world, multi-step threat trajectories rather than static benchmarks.

The paper conducts a preliminary safety evaluation of the open-weight LLM Kimi K2.5, finding that while it is highly capable, it exhibits concerning dual-use risks, particularly regarding CBRNE misuse…

This paper proposes the first web-focused threat model for agentic browsers, demonstrating that traditional web social engineering attacks can be amplified into dangerous, reproducible threats when ex…

The paper introduces MATRA, a systematic threat modeling framework, to assess how known LLM threats translate into concrete, deployment-specific risks within autonomous agentic AI systems.