This study advances the security of swarm robotics by examining the resilience of stigmergic communication in foraging robot swarms against deceptive strategies. We specifically investigate the swarm’s vulnerability to attacks via misleading pheromone trails laid by detractor robots, which significantly hinder foraging performance. Through simulations, we evaluated the adverse effects of such attacks on resource collection and forager capture rates, highlighting a notable decline as the percentage of detractors increases. To counter these threats, we implement a robust defense mechanism utilizing DBSCAN for density-based clustering of pheromone trails, complemented by a cluster grouping method that effectively isolates batches of detractors early in the simulation. This approach incorporates an adaptive timing mechanism to discern and counteract misleading trails, substantially mitigating forager captures and enhancing swarm foraging efficiency. Furthermore, we extend our analysis by introducing obstacles in the simulation environment to test the defense’s robustness under varied and complex conditions. These experiments demonstrate that our defense strategy remains effective, maintaining operational stability even when faced with additional environmental challenges. This research not only underscores critical security vulnerabilities in pheromone-based foraging algorithms but also sets the foundation for developing more secure and resilient swarm robotics systems for real-world applications where robustness against both deceptive strategies and environmental complexities is essential.