TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion
Saideep Sreekumar, Zeng Wang, Akashdeep Saha, Weihua Xiao, Minghao Shao, Muhammad Shafique, Ozgur Sinanoglu, Ramesh Karri, Johann Knechtel
cs.CR, cs.AI, cs.AR
Submitted: 2026-08-19
Updated: 2026-08-20
Terminology
Sources
- SAND: A Self-supervised and Adaptive NAS-Driven Framework for Hardware Trojan Detection
- TrojanForge: Generating Adversarial Hardware Trojan Examples Using Reinforcement Learning
- NetDeTox: Adversarial and Efficient Evasion of Hardware-Security GNNs via RL-LLM Orchestration
- Automatic Hardware Trojan Insertion using Machine Learning
- LockForge: Automating Paper-to-Code for Logic Locking with Multi-Agent Reasoning LLMs
- DeepRTL2: A Versatile Model for RTL-Related Tasks
- VeriContaminated: Assessing LLM-Driven Verilog Coding for Data Contamination
- VeriLeaky: Navigating IP Protection vs Utility in Fine-Tuning for LLM-Driven Verilog Coding
- SENTAUR: Security EnhaNced Trojan Assessment Using LLMs Against Undesirable Revisions
- Trojan Playground: A Reinforcement Learning Framework for Hardware Trojan Insertion and Detection
- SALAD: Systematic Assessment of Machine Unlearning on LLM-Aided Hardware Design
- TrojanWhisper: Evaluating Pre-trained LLMs to Detect and Localize Hardware Trojans
- TrojanLoC: LLM-based Framework for RTL Trojan Localization
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