Adversarial Attacks
Showing 4 of 4 projects
A centralized repository summarizing practical and proposed defenses against prompt injection attacks on large language models.
A curated list of resources for understanding, detecting, and mitigating prompt injection attacks against machine learning models.
A comprehensive survey and unified safety framework for embodied AI, covering 400+ papers on risks, attacks, and defenses across perception, cognition, planning, interaction, and agentic systems.
A PyTorch-based toolbox for graph reliability, focusing on adversarial attacks, defenses, and robustness techniques for graph neural networks.
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