DUDE framework reduces web agents' susceptibility to deceptive UIs by 53.8% on a new 1,407-scenario benchmark while preserving task performance.
Evaluating the robustness of multimodal agents against active environmental injection attacks
4 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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LaSM is a layer-wise scaling mechanism that amplifies attention and MLP modules in critical layers to defend GUI agents against pop-up attacks by correcting attention misalignment.
A survey that taxonomizes threats to agentic AI, reviews benchmarks and evaluation methods, discusses technical and governance defenses, and identifies open challenges.
A synthesis of 247 papers on LLM agent security identifies prompt injection and tool hijacking as dominant threats, notes weakly compositional defenses, and argues for trust boundaries and realistic evaluations.
citing papers explorer
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Don't Click That: Teaching Web Agents to Resist Deceptive Interfaces
DUDE framework reduces web agents' susceptibility to deceptive UIs by 53.8% on a new 1,407-scenario benchmark while preserving task performance.
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LaSM: Layer-wise Scaling Mechanism for Defending Pop-up Attack on GUI Agents
LaSM is a layer-wise scaling mechanism that amplifies attention and MLP modules in critical layers to defend GUI agents against pop-up attacks by correcting attention misalignment.
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Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges
A survey that taxonomizes threats to agentic AI, reviews benchmarks and evaluation methods, discusses technical and governance defenses, and identifies open challenges.
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Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation
A synthesis of 247 papers on LLM agent security identifies prompt injection and tool hijacking as dominant threats, notes weakly compositional defenses, and argues for trust boundaries and realistic evaluations.