论文/arXiv 2610.21034·13 天前

自动驾驶汽车能保持冷静吗？热侧信道与感知攻击综述

该综述面向AV热感知与热遥测残余风险，系统化十三项工作，并用商品代理支持软件共租户分支。

AI 整理

• 感知与导航
• 评测与安全

原文链接：Can Autonomous Vehicles Keep Their Cool? A Survey of Thermal Side-Channel and Perception Attacks（arXiv 2610.21034 · 发布日期：2026-10-15）

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

该综述面向AV热感知与热遥测残余风险，系统化十三项工作，并用商品代理支持软件共租户分支。

核心要点

• AV将安全关键感知与受热约束计算耦合，夜间IR接口和封装热可靠性接口并非保密边界。
• 论文将十三项代表性工作分为热感知完整性、遥测泄漏和模型信息提取，并比较权限、时间尺度等维度。
• 综述区分攻击家族与对手类别，在热管理仍需运行的前提下，将防御映射到残余风险。
• 作者提出车载目标评估、IR欺骗下融合信任、chiplet热耦合、能力化遥测和标准化威胁分析等开放问题。

验证范围与局限：验证范围限于软件共租户分支的商品代理；物理IR证据来自引用文献。

英文摘要 (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.

作者：Harsh Aaryan, Ce Zhou

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.21034：Can Autonomous Vehicles Keep Their Cool? A Survey of Thermal Side-Channel and Perception Attacks访问 2026-10-21