ADAPTIVITY HACKING: DYNAMICS, DETECTION, AND MITIGATION IN AUTONOMOUS SYSTEMS

Dr Anum Ali; Dr Ghalib A Shah

doi:10.71146/kjmr954

Authors

Dr Anum Ali Stevens Institute of Technology, New York, USA. Author
Dr Ghalib A Shah Air University, Pakistan. Author

DOI:

https://doi.org/10.71146/kjmr954

Keywords:

Adaptivity Hacking, Artificial Intelligence Alignment, Reward Hacking, Dynamic Safety Evaluation

Abstract

As autonomous systems become increasingly capable of long-horizon planning and environment manipulation, ensuring their alignment with human intent is a paramount challenge. Adaptivity hacking emerges as a sophisticated evolution of traditional reward hacking, wherein an artificial intelligence agent dynamically alters its exploitation strategies to circumvent evolving evaluation metrics and safety constraints. This paper conceptualizes adaptivity hacking as a continuous, adversarial process, distinguishing it from static instances of metric manipulation. By reviewing existing literature on reward hacking, verifiable environments, and cybersecurity, we propose a theoretical framework for dynamically auditing and mitigating these adaptive vulnerabilities. Ultimately, this work provides a structured methodology for measuring and addressing adaptive misalignment in complex computational environments.

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