# ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning

arXiv: https://arxiv.org/abs/2208.12897 · HTML: https://mlsanity.com/paper/2208.12897/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 80 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Reinforcement Learning for Cyber Security](https://mlsanity.com/paper/1906.05799/) (2019)
- [IoT Security Techniques Based on Machine Learning](https://mlsanity.com/paper/1801.06275/) (2018)
- [Deep Reinforcement Fuzzing](https://mlsanity.com/paper/1801.04589/) (2018)
- [Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection](https://mlsanity.com/paper/2009.05602/) (2020)
- [Hardware Trojan Insertion Using Reinforcement Learning](https://mlsanity.com/paper/2204.04350/) (2022)

## Built on by (most cited first, 11 of 11)

- [Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning](https://mlsanity.com/paper/2312.04940/) (2023)
- [AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning](https://mlsanity.com/paper/2402.13946/) (2024)
- [SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity](https://mlsanity.com/paper/2602.08690/) (2026)
- [Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges](https://mlsanity.com/paper/2208.13885/) (2022)
- [Building Better Environments for Autonomous Cyber Defence](https://mlsanity.com/paper/2604.08805/) (2026)
- [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)
- [GUIDE: GenAI Units In Digital Design Education](https://mlsanity.com/paper/2603.17296/) (2026)
- [TroLLoc: Logic Locking and Layout Hardening for IC Security Closure against Hardware Trojans](https://mlsanity.com/paper/2405.05590/) (2024)
- [TrojanForge: Generating Adversarial Hardware Trojan Examples Using Reinforcement Learning](https://mlsanity.com/paper/2405.15184/) (2024)
- [Protocol effects on feature-based hardware-Trojan detection across Trust-Hub families](https://mlsanity.com/paper/2609.07199/) (2026)

## Abstract (arXiv)

Stealthy hardware Trojans (HTs) inserted during the fabrication of integrated circuits can bypass the security of critical infrastructures. Although researchers have proposed many techniques to detect HTs, several limitations exist, including: (i) a low success rate, (ii) high algorithmic complexity, and (iii) a large number of test patterns. Furthermore, the most pertinent drawback of prior detection techniques stems from an incorrect evaluation methodology, i.e., they assume that an adversary inserts HTs randomly. Such inappropriate adversarial assumptions enable detection techniques to claim high HT detection accuracy, leading to a "false sense of security." Unfortunately, to the best of our knowledge, despite more than a decade of research on detecting HTs inserted during fabrication, there have been no concerted efforts to perform a systematic evaluation of HT detection techniques. In this paper, we play the role of a realistic adversary and question the efficacy of HT detection techniques by developing an automated, scalable, and practical attack framework, ATTRITION, using reinforcement learning (RL). ATTRITION evades eight detection techniques across two HT detection categories, showcasing its agnostic behavior. ATTRITION achieves average attack success rates of 47× and 211× compared to randomly inserted HTs against state-of-the-art HT detection techniques. We demonstrate ATTRITION's ability to evade detection techniques by evaluating designs ranging from the widely-used academic suites to larger designs such as the open-source MIPS and mor1kx processors to AES and a GPS module. Additionally, we showcase the impact of ATTRITION-generated HTs through two case studies (privilege escalation and kill switch) on the mor1kx processor. We envision that our work, along with our released HT benchmarks and models, fosters the development of better HT detection techniques.