A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Yifan Yao, Jinhao Duan, Kaidi Xu, et al.
A comprehensive survey categorizing LLM security and privacy research into beneficial applications, offensive uses, and inherent vulnerabilities with defenses.
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This paper introduces VPI-Bench, a benchmark for evaluating visual prompt injection attacks against computer-use agents, revealing significant vulnerabilities in current systems.
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Introduces HackWorld, a benchmark for evaluating computer-use agents on exploiting web application vulnerabilities, analyzing task completion and tool usage across models and observation spaces.
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This paper demonstrates that state-of-the-art Process Reward Models (PRMs) are systematically exploitable, revealing critical robustness vulnerabilities in LLM reasoning pipelines.
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This paper analyzes the security of large language model-based AI agents by studying their workflows and identifying vulnerabilities.