arXiv AI
AdvSim2Real : Training Web Agents Against Adaptive Prompt Injection in a Web World Model
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CoER: Defending against Adaptive Indirect Prompt Injection via Adversarial Co-Evolution and Refinement
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Hugging Face Trending Papers
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Defending against Adaptive Prompt Injection Attacks via Reasoning-enabled Task Alignment
Indirect prompt injection attacks hijack LLM-based agents by embedding malicious instructions in third-party data that the agent retrieves during task execution. Existing defenses report near-zero attack success rate on static benchmarks, yet recent adaptive evaluations show that these results collapse once the attacker is allowed to optimize against the deployed defense.
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Training Needs Trustworthy Worlds: Verified Synthetic Web Environments for Agent Learning
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Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents
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