  Collaborative perception, which greatly enhances the sensing capability of
connected and autonomous vehicles (CAVs) by incorporating data from external
resources, also brings forth potential security risks. CAVs' driving decisions
rely on remote untrusted data, making them susceptible to attacks carried out
by malicious participants in the collaborative perception system. However,
security analysis and countermeasures for such threats are absent. To
understand the impact of the vulnerability, we break the ground by proposing
various real-time data fabrication attacks in which the attacker delivers
crafted malicious data to victims in order to perturb their perception results,
leading to hard brakes or increased collision risks. Our attacks demonstrate a
high success rate of over 86% on high-fidelity simulated scenarios and are
realizable in real-world experiments. To mitigate the vulnerability, we present
a systematic anomaly detection approach that enables benign vehicles to jointly
reveal malicious fabrication. It detects 91.5% of attacks with a false positive
rate of 3% in simulated scenarios and significantly mitigates attack impacts in
real-world scenarios.
