Papers with proactive protection

2 papers
MemoPhishAgent: Memory-Augmented Multi-Modal LLM Agent for Phishing URL Detection (2026.acl-industry)

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Challenge: Traditional phishing website detection relies on static heuristics or reference lists, which lag behind rapidly evolving attacks.
Approach: They propose a memory-augmented multi-modal LLM agent that leverages episodic memories to guide decisions on recurring and novel threats.
Outcome: The proposed agent outperforms state-of-the-art phishing detection tools on two public datasets and improves recall by 20%.
Revisiting Early Detection of Sexual Predators via Turn-level Optimization (2025.naacl-long)

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Challenge: Existing methods to detect online grooming rely on chat-level risk labels and fail to identify optimal intervention points.
Approach: They propose a speed control reinforcement learning strategy based on luring communication theory to capture the predator’s turn-level entrapment and a new reward function that balances the trade-off between speed and accuracy based upon the LCT.
Outcome: The proposed method preempts online grooming while identifying optimal early intervention points.

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