# Law and Adversarial Machine Learning

arXiv: https://arxiv.org/abs/1810.10731 · HTML: https://mlsanity.com/paper/1810.10731/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2018.

## Built on by (most cited first, 5 of 5)

- [The Many Faces of Adversarial Risk](https://mlsanity.com/paper/2201.08956/) (2022)
- [On managing vulnerabilities in AI/ML systems](https://mlsanity.com/paper/2101.10865/) (2021)
- [Efficient Learning of Interpretable Classification Rules](https://mlsanity.com/paper/2205.06936/) (2022)
- [You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks](https://mlsanity.com/paper/2306.09951/) (2023)
- [On Feasibility of Intent Obfuscating Attacks](https://mlsanity.com/paper/2408.02674/) (2024)

## Abstract (arXiv)

When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond? Through scenarios grounded in adversarial ML literature, we explore how some aspects of computer crime, copyright, and tort law interface with perturbation, poisoning, model stealing and model inversion attacks to show how some attacks are more likely to result in liability than others. We end with a call for action to ML researchers to invest in transparent benchmarks of attacks and defenses; architect ML systems with forensics in mind and finally, think more about adversarial machine learning in the context of civil liberties. The paper is targeted towards ML researchers who have no legal background.