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.

Adaptive Bitrate (ABR) decision-making is essential for optimizing Quality of Experience (QoE) in video streaming. Traditional ABR methods typically lack fine-grained content understanding and face challenges in balancing multi-dimensional QoE objectives under real-world edge device constraints. To overcome these limitations, we propose **StreamSense Agent**, a novel framework that reformulates ABR as a sequential decision-making process informed by content and network awareness. Our approach introduces three key innovations: (1) a Dynamic Content-Aware Entity Graph (D-CAEG) for structured video representation, (2) a composite QoE-driven optimization objective integrating perceptual quality, rebuffering, smoothness, and computational cost, and (3) a hierarchical resource-aware architecture enabling real-time control through an efficient lightweight actor. We further adopt an offline-to-online co-adaptation training paradigm with federated learning for continuous adaptation. Since effective ABR control requires the same core competencies as general sequential decision-making—multi-step planning across future video chunks, dynamic information gathering from heterogeneous network signals, and adaptive strategy adjustment under resource constraints—we validate these transferable capabilities on five established benchmarks. Specifically, multi-hop search tasks (BrowseComp, BrowseComp-ZH) mirror the integration of dispersed network-trace and content-complexity signals into a bitrate plan; complex navigation tasks (WebWalkerQA) correspond to sequential buffer-state traversal under volatile bandwidth; and information-extraction tasks (Xbench-DS, SimpleQA) reflect the precision required when reading real-time streaming telemetry. StreamSense Agent achieves state-of-the-art results, attaining accuracy of 16.7% on BrowseComp, 30.5% on BrowseComp-ZH, 42.1% on Xbench-DS, and 54.2% on WebWalkerQA. Deployment-oriented evaluations further confirm its computational efficiency, achieving an inference throughput of 12.5 requests per second with a memory footprint of only 2,100 MB under edge-like constraints, yielding a normalized QoE score of 0.92. Comprehensive ablation studies validate the necessity of each proposed component. This work establishes a new paradigm for intelligent, edge-deployable bitrate adaptation.