Baiting AI: Deceptive Adversary Against AI-Protected Industrial Infrastructures
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908761564643328 |
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| author | Pasikhani, Aryan Gope, Prosanta Yang, Yang Mehnaz, Shagufta Sikdar, Biplab |
| author_facet | Pasikhani, Aryan Gope, Prosanta Yang, Yang Mehnaz, Shagufta Sikdar, Biplab |
| contents | This paper explores a new cyber-attack vector targeting Industrial Control Systems (ICS), particularly focusing on water treatment facilities. Developing a new multi-agent Deep Reinforcement Learning (DRL) approach, adversaries craft stealthy, strategically timed, wear-out attacks designed to subtly degrade product quality and reduce the lifespan of field actuators. This sophisticated method leverages DRL methodology not only to execute precise and detrimental impacts on targeted infrastructure but also to evade detection by contemporary AI-driven defence systems. By developing and implementing tailored policies, the attackers ensure their hostile actions blend seamlessly with normal operational patterns, circumventing integrated security measures. Our research reveals the robustness of this attack strategy, shedding light on the potential for DRL models to be manipulated for adversarial purposes. Our research has been validated through testing and analysis in an industry-level setup. For reproducibility and further study, all related materials, including datasets and documentation, are publicly accessible. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08481 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Baiting AI: Deceptive Adversary Against AI-Protected Industrial Infrastructures Pasikhani, Aryan Gope, Prosanta Yang, Yang Mehnaz, Shagufta Sikdar, Biplab Cryptography and Security This paper explores a new cyber-attack vector targeting Industrial Control Systems (ICS), particularly focusing on water treatment facilities. Developing a new multi-agent Deep Reinforcement Learning (DRL) approach, adversaries craft stealthy, strategically timed, wear-out attacks designed to subtly degrade product quality and reduce the lifespan of field actuators. This sophisticated method leverages DRL methodology not only to execute precise and detrimental impacts on targeted infrastructure but also to evade detection by contemporary AI-driven defence systems. By developing and implementing tailored policies, the attackers ensure their hostile actions blend seamlessly with normal operational patterns, circumventing integrated security measures. Our research reveals the robustness of this attack strategy, shedding light on the potential for DRL models to be manipulated for adversarial purposes. Our research has been validated through testing and analysis in an industry-level setup. For reproducibility and further study, all related materials, including datasets and documentation, are publicly accessible. |
| title | Baiting AI: Deceptive Adversary Against AI-Protected Industrial Infrastructures |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2601.08481 |