TACTIC: Temporal and Context-Aware LLM Tactical Planning for Roadside LiDAR Attacks

Summary

TACTIC is a scene-aware framework that uses a multimodal large language model to coordinate roadside LiDAR attacks under a gray-box threat model, without access to the victim LiDAR's point clouds or internal processing. Across 280 randomized CARLA trials, the full policy achieved a 100% collision rate, compared with 35% for a fixed rule, 60% for random selection, and 75% for a restricted LLM using mode selection with default parameters. The authors conclude that scene-dependent tactical planning can expose context-sensitive LiDAR failure modes that fixed attack policies may miss.

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