  Object detection tasks, crucial in safety-critical systems like autonomous
driving, focus on pinpointing object locations. These detectors are known to be
susceptible to backdoor attacks. However, existing backdoor techniques have
primarily been adapted from classification tasks, overlooking deeper
vulnerabilities specific to object detection. This paper is dedicated to
bridging this gap by introducing Detector Collapse} (DC), a brand-new backdoor
attack paradigm tailored for object detection. DC is designed to instantly
incapacitate detectors (i.e., severely impairing detector's performance and
culminating in a denial-of-service). To this end, we develop two innovative
attack schemes: Sponge for triggering widespread misidentifications and
Blinding for rendering objects invisible. Remarkably, we introduce a novel
poisoning strategy exploiting natural objects, enabling DC to act as a
practical backdoor in real-world environments. Our experiments on different
detectors across several benchmarks show a significant improvement
($\sim$10\%-60\% absolute and $\sim$2-7$\times$ relative) in attack efficacy
over state-of-the-art attacks.
