LATIDIA · Robótica

TÁCTICA: Planificación táctica de LLM temporal y consciente del contexto para ataques LiDAR en carretera

arXiv:2609.39969v1 Tipo de anuncio: nuevo Resumen: Los ataques LiDAR físicos a menudo se evalúan utilizando primitivas fijas y parámetros seleccionados manualmente, a pesar de su fuerte dependencia del tráfico circundante. Presentamos TACTI

La noticia

arXiv:2609.39969v1 Announce Type: new Abstract: Physical LiDAR attacks are often evaluated using fixed primitives and manually selected parameters, despite their strong dependence on surrounding traffic. We present TACTIC, a scene-aware framework that uses a multimodal large language model (MLLM) to coordinate state-adaptive roadside LiDAR attacks. Under a gray-box threat model, TACTIC relies only on an attacker-operated roadside perception stack, without accessing the victim LiDAR's native point clouds or internal processing. Local perception provides metric vehicle states, while the MLLM combines these measurements with roadside imagery to infer relational traffic context and construct a semantic scene graph. Based on this representation, TACTIC selects and configures two complementary primitives: \emph{push-away}, which shifts