# Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges

arXiv: https://arxiv.org/abs/2208.13885 · HTML: https://mlsanity.com/paper/2208.13885/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 29 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [DRiLLS: Deep Reinforcement Learning for Logic Synthesis](https://mlsanity.com/paper/1911.04021/) (2019)
- [A Deep Reinforcement Learning Approach for Global Routing](https://mlsanity.com/paper/1906.08809/) (2019)
- [Deep Reinforcement Fuzzing](https://mlsanity.com/paper/1801.04589/) (2018)
- [ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning](https://mlsanity.com/paper/2208.12897/) (2022)
- [Hardware Trojan Insertion Using Reinforcement Learning](https://mlsanity.com/paper/2204.04350/) (2022)
- [AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection](https://mlsanity.com/paper/2204.06117/) (2022)
- [DETERRENT: Detecting Trojans using Reinforcement Learning](https://mlsanity.com/paper/2208.12878/) (2022)

## Built on by (most cited first, 3 of 3)

- [AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning](https://mlsanity.com/paper/2402.13946/) (2024)
- [Agentic AI for Autonomous Defense in Software Supply Chain Security: Beyond Provenance to Vulnerability Mitigation](https://mlsanity.com/paper/2512.23480/) (2025)
- [Resource-Optimized and Energy-Aware Agentic AI Framework Anchored on Blockchain for Secure Software Supply Chains](https://mlsanity.com/paper/2609.31282/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) is a machine learning paradigm where an autonomous agent learns to make an optimal sequence of decisions by interacting with the underlying environment. The promise demonstrated by RL-guided workflows in unraveling electronic design automation problems has encouraged hardware security researchers to utilize autonomous RL agents in solving domain-specific problems. From the perspective of hardware security, such autonomous agents are appealing as they can generate optimal actions in an unknown adversarial environment. On the other hand, the continued globalization of the integrated circuit supply chain has forced chip fabrication to off-shore, untrustworthy entities, leading to increased concerns about the security of the hardware. Furthermore, the unknown adversarial environment and increasing design complexity make it challenging for defenders to detect subtle modifications made by attackers (a.k.a. hardware Trojans). In this brief, we outline the development of RL agents in detecting hardware Trojans, one of the most challenging hardware security problems. Additionally, we outline potential opportunities and enlist the challenges of applying RL to solve hardware security problems.