太害怕驾驶：系统发现物理世界攻击下自动驾驶规划中的语义DoS漏洞
Too Afraid to Drive: Systematic Discovery of Semantic DoS Vulnerability in Autonomous Driving Planning under Physical-World Attacks
摘要：
在高级自动驾驶（AD）系统中，行为规划负责做出高级驾驶决策，例如巡航和停车，因此高度安全。在这项工作中，我们进行了第一次系统的研究语义安全漏洞特定于过于保守的AD行为规划行为，即，那些可能导致失败或显著降低任务性能的任务，这对于自动驾驶服务（如机器人出租车/交付）至关重要。我们称之为语义拒绝服务（DoS）的漏洞，我们设想是最普遍暴露在实际的AD系统，由于保守的倾向，以避免安全事故。为了实现高度的实用性和真实性，我们假设攻击者只能将无害的外部物理对象引入驾驶环境，例如，丢弃在路边的纸板箱为了系统地发现这些漏洞，我们设计了PlanFuzz，这是一种新颖的动态测试方法，可以解决各种特定问题的设计挑战。具体来说，我们提出并确定规划不变量作为新的测试神谕，并设计新的输入生成系统地执行特定问题的限制攻击者引入的物理对象。我们还设计了一种新的行为规划漏洞距离度量，以有效地指导发现。我们评估了PlanFuzz在3个实际的开源AD系统的规划实现，并发现它可以有效地发现9个以前未知的语义拒绝服务漏洞，而没有误报。我们发现我们所有的新设计都是必要的，因为如果没有每个设计，通常会观察到统计上显着的性能下降。我们进一步使用模拟和真实车辆跟踪进行开发案例研究。我们讨论了根本原因和潜在的修复方法。
英文摘要：
In high-level Autonomous Driving (AD) systems, behavioral planning is in charge of making high-level driving decisions such as cruising and stopping, and thus highly securitycritical. In this work, we perform the first systematic study of semantic security vulnerabilities specific to overly-conservative AD behavioral planning behaviors, i.e., those that can cause failed or significantly-degraded mission performance, which can be critical for AD services such as robo-taxi/delivery. We call them semantic Denial-of-Service (DoS) vulnerabilities, which we envision to be most generally exposed in practical AD systems due to the tendency for conservativeness to avoid safety incidents. To achieve high practicality and realism, we assume that the attacker can only introduce seemingly-benign external physical objects to the driving environment, e.g., off-road dumped cardboard boxes. To systematically discover such vulnerabilities, we design PlanFuzz, a novel dynamic testing approach that addresses various problem-specific design challenges. Specifically, we propose and identify planning invariants as novel testing oracles, and design new input generation to systematically enforce problemspecific constraints for attacker-introduced physical objects. We also design a novel behavioral planning vulnerability distance metric to effectively guide the discovery. We evaluate PlanFuzz on 3 planning implementations from practical open-source AD systems, and find that it can effectively discover 9 previouslyunknown semantic DoS vulnerabilities without false positives. We find all our new designs necessary, as without each design, statistically significant performance drops are generally observed. We further perform exploitation case studies using simulation and real-vehicle traces. We discuss root causes and potential fixes.
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