Modern edge AI systems rely on dynamic, multi-stage pipelines vulnerable to adversarial attacks. Existing attacks primarily target static tasks and lack adaptability to these complex environments. We propose Hydra, an adversarial attack framework for AI-driven video streaming and edge security services. Hydra integrates three innovations: Multi-Stage Service Disruption (MSSD) that coherently misleads successive pipeline components, Resource-Aware Transferability Enhancement (RATE) for effective attacks under edge constraints, and Meta-Reinforcement Learning for Adaptive Perturbation (MetaRAP) that dynamically tunes attack parameters based on real-time system states. Evaluations on a multimodal medical RAG system demonstrate Hydra’s superior efficacy, achieving up to 75% ASR against specialized retrievers at 𝜖=8/255, a 12% absolute improvement over baselines. Hydra also exhibits strong transferability, defense robustness, and a favorable efficiency trade-off, requiring only 2.1 seconds per adversarial example. This work highlights a potent threat to edge intelligence services, urging development of robust multi-stage defenses.