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For high-level Autonomous Vehicles (AV), localization is highly security andsafety critical. One direct threat to it is GPS spoofing, but fortunately, AVsystems today predominantly use Multi-Sensor Fusion (MSF) algorithms that aregenerally believed to have the potential to practically defeat GPS spoofing.However, no prior work has studied whether today's MSF algorithms are indeedsufficiently secure under GPS spoofing, especially in AV settings. In thiswork, we perform the first study to fill this critical gap. As the first study,we focus on a production-grade MSF with both design and implementation levelrepresentativeness, and identify two AV-specific attack goals, off-road andwrong-way attacks.  To systematically understand the security property, we first analyze theupper-bound attack effectiveness, and discover a take-over effect that canfundamentally defeat the MSF design principle. We perform a cause analysis andfind that such vulnerability only appears dynamically andnon-deterministically. Leveraging this insight, we design FusionRipper, a noveland general attack that opportunistically captures and exploits take-overvulnerabilities. We evaluate it on 6 real-world sensor traces, and find thatFusionRipper can achieve at least 97% and 91.3% success rates in all traces foroff-road and wrong-way attacks respectively. We also find that it is highlyrobust to practical factors such as spoofing inaccuracies. To improve thepracticality, we further design an offline method that can effectively identifyattack parameters with over 80% average success rates for both attack goals,with the cost of at most half a day. We also discuss promising defensedirections.

Drift with Devil: Security of Multi-Sensor Fusion based Localization in High-Level Autonomous Driving under GPS Spoofing (Extended Version)