Millimeter-wave (mmWave) radars are indispensable components of safety-critical advanced driver assistance systems, enabling accurate and weather-resilient environmental sensing for autonomous vehicles. Despite the advanced sensing capabilities, mmWave radars remain susceptible to adversarial attacks, where malicious users attempt to distort the sensing results of victim radars, leading to hazardous driving behaviors. While existing anti-spoofing techniques have been proposed to mitigate specific attacks, they may be ineffective against adaptive adversaries. To address this critical vulnerability, we introduce AttackDeceiver, a novel anti-spoofing system using a phase-shifted interleaving waveform. By comparing range and velocity estimates from two independent virtual channels, our system effectively detects and mitigates the effects of spoofing attacks. In addition, we proactively counter adaptive spoofing attacks by inducing attackers to generate false targets with unrealistic velocity fluctuations. A compact prototype of AttackDeceiver is realized using commercial-off-the-shelf radar kits. Experimental results demonstrate the effectiveness of our system, achieving a remarkable false target recall exceeding$97.9\%$and a significant enhancement in signal-to-interference-plus-noise ratio exceeding$\text{13.46}\,\text{dB}$.