@inproceedings{li-etal-2026-hydra,
title = "Hydra: Adaptive Adversarial Attacks for Dynamic Multi-Stage Edge Intelligence Services",
author = "Li, Linhan and
Jiang, Chuanqi and
Weng, Wenqian and
Pang, Jacqueline J. and
Tao, Zhiguo",
editor = "Kamalloo, Ehsan and
Gontier, Nicolas and
Rozanov, Nikolai and
Hashemzadeh, Maryam and
Arora, Akhil",
booktitle = "Proceedings of the Second Workshop for {RE}search on Agent Language Models ({REALM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.realm-1.2/",
pages = "14--26",
ISBN = "979-8-89176-485-9",
abstract = "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 $\epsilon=8/255$, 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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Hydra: Adaptive Adversarial Attacks for Dynamic Multi-Stage Edge Intelligence Services
%A Li, Linhan
%A Jiang, Chuanqi
%A Weng, Wenqian
%A Pang, Jacqueline J.
%A Tao, Zhiguo
%Y Kamalloo, Ehsan
%Y Gontier, Nicolas
%Y Rozanov, Nikolai
%Y Hashemzadeh, Maryam
%Y Arora, Akhil
%S Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-485-9
%F li-etal-2026-hydra
%X 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.
%U https://aclanthology.org/2026.realm-1.2/
%P 14-26
Markdown (Informal)
[Hydra: Adaptive Adversarial Attacks for Dynamic Multi-Stage Edge Intelligence Services](https://aclanthology.org/2026.realm-1.2/) (Li et al., REALM 2026)
ACL