# Trojan Playground: A Reinforcement Learning Framework for Hardware Trojan Insertion and Detection

arXiv: https://arxiv.org/abs/2305.09592 · HTML: https://mlsanity.com/paper/2305.09592/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2023.

## Built on by (most cited first, 4 of 4)

- [SENTAUR: Security EnhaNced Trojan Assessment Using LLMs Against Undesirable Revisions](https://mlsanity.com/paper/2407.12352/) (2024)
- [TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion](https://mlsanity.com/paper/2601.17178/) (2026)
- [The Seeker's Dilemma: Realistic Formulation and Benchmarking for Hardware Trojan Detection](https://mlsanity.com/paper/2402.17918/) (2024)
- [TrojanForge: Generating Adversarial Hardware Trojan Examples Using Reinforcement Learning](https://mlsanity.com/paper/2405.15184/) (2024)

## Abstract (arXiv)

Current Hardware Trojan (HT) detection techniques are mostly developed based on a limited set of HT benchmarks. Existing HT benchmark circuits are generated with multiple shortcomings, i.e., i) they are heavily biased by the designers' mindset when created, and ii) they are created through a one-dimensional lens, mainly the signal activity of nets. We introduce the first automated Reinforcement Learning (RL) HT insertion and detection framework to address these shortcomings. In the HT insertion phase, an RL agent explores the circuits and finds locations best for keeping inserted HTs hidden. On the defense side, we introduce a multi-criteria RL-based HT detector that generates test vectors to discover the existence of HTs. Using the proposed framework, one can explore the HT insertion and detection design spaces to break the limitations of human mindset and benchmark issues, ultimately leading toward the next generation of innovative detectors. We demonstrate the efficacy of our framework on ISCAS-85 benchmarks, provide the attack and detection success rates, and define a methodology for comparing our techniques.