Advances in AI-Powered Agent Security and Evaluation

The field of AI-powered agents is rapidly evolving, with a growing focus on security and evaluation. Recent research has highlighted the importance of testing and reliability assurance for AI-powered browser extensions, as well as the need for comprehensive frameworks for evaluating agent behavior. Notable advancements include the development of novel testing frameworks, such as ASSURE, and the introduction of new evaluation benchmarks, like OpenAgentSafety.Furthermore, researchers have identified significant security vulnerabilities in AI-powered agents, including the potential for backdoor attacks and inter-agent trust exploitation. To address these concerns, there is a growing need for secure deployment paradigms and comprehensive vulnerability assessments.The development of new GUI agents and multimodal large language models has also led to significant advancements in areas like visual grounding and production-living simulations. However, these advancements also introduce new challenges, such as the need for more effective testing and evaluation frameworks.Papers like ASSURE and OpenAgentSafety are particularly noteworthy, as they provide innovative solutions to the challenges of testing and evaluating AI-powered agents. Additionally, papers like VisualTrap and StarDojo highlight the importance of considering security risks and evaluating agent behavior in complex, real-world environments.

Sources

ASSURE: Metamorphic Testing for AI-powered Browser Extensions

A Systematization of Security Vulnerabilities in Computer Use Agents

Inaugural MOASEI Competition at AAMAS'2025: A Technical Report

AI Agent Smart Contract Exploit Generation

R-VLM: Region-Aware Vision Language Model for Precise GUI Grounding

MobileGUI-RL: Advancing Mobile GUI Agent through Reinforcement Learning in Online Environment

GTA1: GUI Test-time Scaling Agent

OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety

Hidden Prompts in Manuscripts Exploit AI-Assisted Peer Review

We Urgently Need Privilege Management in MCP: A Measurement of API Usage in MCP Ecosystems

Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms

The Dark Side of LLMs Agent-based Attacks for Complete Computer Takeover

VisualTrap: A Stealthy Backdoor Attack on GUI Agents via Visual Grounding Manipulation

StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production-Living Simulations with Stardew Valley

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