Jiahao Chen

This paper argues that much of the existing research on Federated Learning (FL) security is based on idealized assumptions, and provides a practical evaluation framework showing that real-world attack performance is often less severe and more unstable than predicted.

SnapGuard proposes a lightweight, multimodal method to detect prompt injection attacks in screenshot-based web agents by analyzing visual stability and contrast-polarity textual signals, achieving high accuracy with significantly reduced computational overhead.

The paper proposes IntraGuard, a black-box, venue-agnostic defense framework that embeds hidden instructions into manuscripts via PDF structure to disrupt AI-generated peer reviews, achieving up to 84% defense success.

The paper introduces the PrivacyIceberg framework to systematically categorize and empirically demonstrate the high risk of automated, deep personal profiling using LLM agents, revealing a significant gap between public concern and platform safeguards.

This paper systematically investigates how various plasticity interventions affect the vulnerability of deep reinforcement learning agents to backdoor attacks, finding that most interventions mitigate threats while one specific intervention exacerbates them.

The paper introduces a simple, token-efficient vision-language model for generating comprehensive pathology synoptic reports from multiple whole-slide images (WSIs), achieving high performance while significantly reducing computational requirements.

The paper proposes FLAME, a novel framework that detects AI-generated image forgeries by identifying intrinsic energy anomalies caused by the diffusion process, achieving state-of-the-art localization.