{"data":{"id":"2f46e571-0c3c-4abb-991e-e082346d2dd4","title":"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.","solution":"N/A -- no mitigation discussed in source.","labels":["research","security"],"sourceUrl":"https://arxiv.org/abs/2609.39969v1","publishedAt":"2026-09-30T15:33:46.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_evasion"],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":["multimodal large language model (MLLM)","CARLA"],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-09-30T15:33:46.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["safety","integrity"],"aiComponentTargeted":"agent","llmSpecific":true,"classifierConfidence":0.85,"researchCategory":"preprint","atlasIds":null}}