Intelligent traffic signal systems, crucial for intelligent transportation systems, have been widely studied and deployed to enhance vehicle traffic efficiency and reduce air pollution. Unfortunately, intelligent traffic signal systems are at risk of data spoofing attack, causing traffic delays, congestion, and even paralysis. In this paper, we reveal a multivehicle collaborative data spoofing attack to intelligent traffic signal systems and propose a collaborative attack sequence generation model based on multiagent reinforcement learning (RL), aiming to explore efficient and stealthy attacks. Specifically, we first model the spoofing attack based on Partially Observable Markov Decision Process (POMDP) at single and multiple intersections. This involves constructing the state space, action space, and defining a reward function for the attack. Then, based on the attack modeling, we propose an automated approach for generating collaborative attack sequences using the Multi‐Actor‐Attention‐Critic (MAAC) algorithm, a mainstream multiagent RL algorithm. Experiments conducted on the multimodal traffic simulation (VISSIM) platform demonstrate a 15% increase in delay time (DT) and a 40% reduction in attack ratio (AR) compared to the single‐vehicle attack, confirming the effectiveness and stealthiness of our collaborative attack.

Pathfinding becomes an important component in many real-world scenarios, such as popular warehouse systems and autonomous aircraft towing vehicles. With the development of reinforcement learning (RL) especially in the context of asynchronous advantage actor-critic (A3C), pathfinding is undergoing a revolution in terms of efficient parallel learning. Similar to other artificial intelligence-based applications, A3C-based pathfinding is also threatened by the adversarial attack. In this paper, we are the first to study the adversarial attack to A3C, that can unexpectedly wake up longtime retraining mechanism until successful pathfinding. We also discover an attack example generation to launch the attack based on gradient band, in which only one baffle of extremely few unit lengths can successfully perform the attack. Experiments with detailed analysis are conducted to show a high attack success rate of 95% with an average baffle length of 2.95. We also discuss defense suggestions leveraging the insights from our analysis.