Can Autonomous Vehicles Keep Their Cool? A Survey of Thermal Side-Channel and Perception Attacks

Abstract

Autonomous vehicles (AVs) couple safety-critical perception to thermally constrained compute. Interfaces built for night perception and package thermal reliability were never engineered as confidentiality boundaries, and closing one such path leaves the other intact. Physical adversaries can spoof thermal infrared (IR) sensors used for night driving, while unprivileged software co-tenants can fingerprint perception workloads from thermal, dynamic voltage and frequency scaling (DVFS), and thermal event interrupt (TEI) telemetry required for package safety. These paths differ in mechanism and privilege; both exploit interfaces that AV stacks cannot disable without compromising Safety of the Intended Functionality (SOTIF). Classical microarchitectural and cyber-physical systems (CPS) surveys address cache timing, adversarial machine learning, and camera or LiDAR spoofing; no prior survey jointly frames physical thermal IR attacks and unprivileged silicon thermal telemetry as one residual-risk class. This survey systematizes thirteen representative works into thermal perception integrity, telemetry leakage, and model-information extraction, comparing them by privilege, timescale, co-tenancy, deployability, and AV relevance. We distinguish attack family from adversary class and map defenses to residual risk while thermal management remains operational. A commodity proxy supports the software co-tenant branch only; physical IR evidence comes from cited literature. We identify open problems in on-target automotive evaluation, fusion trust under IR spoofing, chiplet thermal coupling, capability-based telemetry, and standards-ready threat analysis, aiming to motivate co-design across sensors, operating systems, silicon, and automotive standards.

Research area