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Large language models (LLMs) have demonstrated remarkable potential with code generation/completion tasks for hardware design.
S. Skorobogatov et al. , “Breakthrough silicon scanning discovers backdoor in military chip,” in Proceedings of the 14th International Conference on Cryptographic Hardware and Embedded Systems , ser. CHES’12, 2012, p. 23–40
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M. Rostami et al. , “A primer on hardware security: Models, methods, and metrics,” Proceedings of the IEEE , vol. 102, no. 8, pp. 1283–1295, 2014
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K. Yang et al. , “A2: Analog malicious hardware,” in 2016 IEEE Symposium on Security and Privacy (SP) , 2016, pp. 18–37
2016
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R. Schuster et al. , “You autocomplete me: Poisoning vulnerabilities in neural code completion,” in 30th USENIX Security Symposium (USENIX Security 21) . USENIX Association, Aug. 2021, pp. 1559–1575
2021
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T. Trippel et al. , “Bomberman: Defining and defeating hardware ticking timebombs at design-time,” in 2021 IEEE Symposium on Security and Privacy (SP) , 2021, pp. 970–986
2021
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J. Knechtel, “Hardware security for and beyond CMOS technology,” in Proc. Int. Symp. Phys. Des. , 2021
2021
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S. Thakur et al. , “Verigen: A large language model for verilog code generation,” ACM TODAES , 2023
2023
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2023
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J. Blocklove et al. , “Chip-chat: Challenges and opportunities in conversational hardware design,” in 2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD) . IEEE, Sep. 2023
2023
Earlier work this paper cites.
M. Liu et al. , “Verilogeval: Evaluating large language models for verilog code generation,” in 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD) . IEEE, 2023, pp. 1–8
2023
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2023
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2023
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
Y. Fu et al. , “Gpt4aigchip: Towards next-generation ai accelerator design automation via large language models,” in 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD) . IEEE, 2023, pp. 1–9
2023
Cited alongside, same era.
2023
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2024
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H. Yang et al. , “A comprehensive overview of backdoor attacks in large language models within communication networks,” IEEE Network , pp. 1–1, 2024
2024
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S. Yan et al. , “An LLM-Assisted Easy-to-Trigger backdoor attack on code completion models: Injecting disguised vulnerabilities against strong detection,” in 33rd USENIX Security Symposium (USENIX Security 24) . Philadelphia, PA: USENIX Association, Aug. 2024, pp. 1795–1812
2024
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R. Zhang et al. , “Instruction backdoor attacks against customized LLMs,” in 33rd USENIX Security Symposium (USENIX Security 24) . Philadelphia, PA: USENIX Association, Aug. 2024, pp. 1849–1866. [Online]. Available: https://www.usenix.org/conference/usenixsecurity24/presentation/zhang-rui
2024
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M. D. Purba et al. , “Software vulnerability detection using large language models,” in 2023 IEEE 34th International Symposium on Software Reliability Engineering Workshops (ISSREW) , 2023, pp. 112–119
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Z. Wang et al. , “Llms and the future of chip design: Unveiling security risks and building trust,” in 2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI) , 2024, pp. 385–390
2024
Cited alongside, same era.
Y. Lu et al. , “Rtllm: An open-source benchmark for design rtl generation with large language model,” in 2024 29th ASP-DAC , 2024, pp. 722–727
2024
Cited alongside, same era.
2024
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2024
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2024
Cited alongside, same era.
H. Wu et al. , “Chateda: A large language model powered autonomous agent for eda,” IEEE TCAD , 2024
2024
Cited alongside, same era.
Closest in time.
J. Knechtel et al. , “Trojan insertion versus layout defenses for modern ICs: Red-versus-blue teaming in a competitive community effort,” IACR Transactions on Cryptographic Hardware and Embedded Systems , vol. 2025, no. 1, pp. 37–77, Dec. 2024. [Online]. Available: https://tches.iacr.org/index.php/TCHES/article/view/11921
2024
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2024
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G. Kokolakis et al. , “Harnessing the power of general-purpose llms in hardware trojan design,” in Applied Cryptography and Network Security Workshops: ACNS 2024 Satellite Workshops . Springer-Verlag, 2024, p. 176–194
2024
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2024
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M. O. Faruque, P. Jamieson, A. Patooghy, and A.-H. A. Badawy, “Unleashing ghost: An llm-powered framework for automated hardware trojan design,” 2024
2024
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