How Far Have We Gone in Binary Code Understanding Using Large Language Models

E. M. Gellenbeck and C. R. Cook, “An investigation of procedure and variable names as beacons during program comprehension,” USA, Tech. Rep., 1991

1991

Earlier work this paper cites.

K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics . Philadelphia, Pennsylvania, USA: Association for Computational Linguistics, 2002, pp. 311–318. [Online]. Available: https://aclanthology.org/P02-1040

2002

Earlier work this paper cites.

J. T. Giffin, S. Jha, and B. P. Miller, “Efficient context-sensitive intrusion detection,” in Network and Distributed System Security Symposium , 2004

2004

Earlier work this paper cites.

C.-Y. Lin, “ROUGE: A package for automatic evaluation of summaries,” in Text Summarization Branches Out . Barcelona, Spain: Association for Computational Linguistics, 2004, pp. 74–81. [Online]. Available: https://aclanthology.org/W04-1013

2004

Earlier work this paper cites.

A. Lavie and M. J. Denkowski, “The meteor metric for automatic evaluation of machine translation,” Machine Translation , vol. 23, no. 2–3, p. 105–115, sep 2009. [Online]. Available: https://doi.org/10.1007/s10590-009-9059-4

2009

Earlier work this paper cites.

G. Sridhara, E. Hill, D. Muppaneni, L. Pollock, and K. Vijay-Shanker, “Towards automatically generating summary comments for java methods,” in Proceedings of the 25th IEEE/ACM International Conference on Automated Software Engineering , ser. ASE ’10. New York, NY, USA: Association for Computing Machinery, 2010, p. 43–52. [Online]. Available: https://doi.org/10.1145/1858996.1859006

2010

Earlier work this paper cites.

I. U. International, “Dwarf debugging information format version 4,” https://dwarfstd.org/doc/DWARF4.pdf , 2010

2010

Earlier work this paper cites.

G. Canfora, M. Di Penta, and L. Cerulo, “Achievements and challenges in software reverse engineering,” Commun. ACM , vol. 54, no. 4, p. 142–151, apr 2011. [Online]. Available: https://doi.org/10.1145/1924421.1924451

2011

Earlier work this paper cites.

J. I. Maletic and M. L. Collard, “Exploration, analysis, and manipulation of source code using srcml,” in 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering , vol. 2, 2015, pp. 951–952

2015

Earlier work this paper cites.

P. Junod, J. Rinaldini, J. Wehrli, and J. Michielin, “Obfuscator-llvm – software protection for the masses,” in Proceedings of the 2015 IEEE/ACM 1st International Workshop on Software Protection , ser. SPRO ’15. USA: IEEE Computer Society, 2015, p. 3–9. [Online]. Available: https://doi.org/10.1109/SPRO.2015.10

Original

2015

Earlier work this paper cites.

X. Meng and B. P. Miller, “Binary code is not easy,” in Proceedings of the 25th International Symposium on Software Testing and Analysis , ser. ISSTA 2016. New York, NY, USA: Association for Computing Machinery, 2016, p. 24–35. [Online]. Available: https://doi.org/10.1145/2931037.2931047

2016

Earlier work this paper cites.

J. Patrick-Evans, L. Cavallaro, and J. Kinder, “Probabilistic naming of functions in stripped binaries,” in Proceedings of the 36th Annual Computer Security Applications Conference , ser. ACSAC ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 373–385. [Online]. Available: https://doi.org/10.1145/3427228.3427265

2020

Earlier work this paper cites.

Y. David, U. Alon, and E. Yahav, “Neural reverse engineering of stripped binaries using augmented control flow graphs,” Proc. ACM Program. Lang. , vol. 4, no. OOPSLA, nov 2020. [Online]. Available: https://doi.org/10.1145/3428293

2020

Earlier work this paper cites.

Y. David, U. Alon, and E. Yahav, “Neural reverse engineering of stripped binaries using augmented control flow graphs,” Proc. ACM Program. Lang. , vol. 4, no. OOPSLA, nov 2020. [Online]. Available: https://doi.org/10.1145/3428293

2020

Earlier work this paper cites.

Z. Feng, D. Guo, D. Tang, N. Duan, X. Feng, M. Gong, L. Shou, B. Qin, T. Liu, D. Jiang, and M. Zhou, “Codebert: A pre-trained model for programming and natural languages,” 2020

2020

Earlier work this paper cites.

Z. Zhang, W. You, G. Tao, Y. Aafer, X. Liu, and X. Zhang, “Stochfuzz: Sound and cost-effective fuzzing of stripped binaries by incremental and stochastic rewriting,” in 2021 IEEE Symposium on Security and Privacy (SP) , 2021, pp. 659–676

2021

Earlier work this paper cites.

H. Gao, S. Cheng, Y. Xue, and W. Zhang, “A lightweight framework for function name reassignment based on large-scale stripped binaries,” in Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis , ser. ISSTA 2021. New York, NY, USA: Association for Computing Machinery, 2021, p. 607–619. [Online]. Available: https://doi.org/10.1145/3460319.3464804

2021

Earlier work this paper cites.

Y. Wang, W. Wang, S. Joty, and S. C. Hoi, “CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, nov 2021, pp. 8696–8708. [Online]. Available: https://aclanthology.org/2021.emnlp-main.685

2021

Earlier work this paper cites.

K. Pei, Z. Xuan, J. Yang, S. Jana, and B. Ray, “Trex: Learning execution semantics from micro-traces for binary similarity,” 2021

2021

Earlier work this paper cites.

X. Li, Y. Qu, and H. Yin, “Palmtree: Learning an assembly language model for instruction embedding,” in Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 3236–3251. [Online]. Available: https://doi.org/10.1145/3460120.3484587

2021

Earlier work this paper cites.

E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” 2021

2021

Earlier work this paper cites.

S. Alrabaee, M. Debbabi, and L. Wang, “A survey of binary code fingerprinting approaches: Taxonomy, methodologies, and features,” ACM Comput. Surv. , vol. 55, no. 1, jan 2022. [Online]. Available: https://doi.org/10.1145/3486860

2022

Earlier work this paper cites.

X. Jin, K. Pei, J. Y. Won, and Z. Lin, “Symlm: Predicting function names in stripped binaries via context-sensitive execution-aware code embeddings,” in Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 1631–1645. [Online]. Available: https://doi.org/10.1145/3548606.3560612

2022

Earlier work this paper cites.

L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, and C. Z. et.al., “Training language models to follow instructions with human feedback,” 2022

2022

Cited alongside, same era.

H. Wang, W. Qu, G. Katz, W. Zhu, Z. Gao, H. Qiu, J. Zhuge, and C. Zhang, “jtrans: jump-aware transformer for binary code similarity detection,” in Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis , ser. ISSTA 2022. New York, NY, USA: Association for Computing Machinery, 2022, p. 1–13. [Online]. Available: https://doi.org/10.1145/3533767.3534367

2022

Cited alongside, same era.

Hex-RaysSA, “"ida pro",” https://www.hex-rays.com/products/ida , 2023

2023

Cited alongside, same era.

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2023

Cited alongside, same era.

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A. Q. Jiang, A. Sablayrolles, A. Roux, A. Mensch, B. Savary, C. Bamford, D. S. Chaplot, D. de las Casas, E. B. Hanna, F. Bressand, G. Lengyel, G. Bour, G. Lample, L. R. Lavaud, L. Saulnier, M.-A. Lachaux, P. Stock, S. Subramanian, S. Yang, S. Antoniak, T. L. Scao, T. Gervet, T. Lavril, T. Wang, T. Lacroix, and W. E. Sayed, “Mixtral of experts,” 2024

2024

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FFmpeg, 2024. [Online]. Available: https://github.com/FFmpeg/FFmpeg

2024

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Redis, 2024. [Online]. Available: https://github.com/redis/redis

2024

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Curl, 2024. [Online]. Available: https://github.com/curl/curl

2024

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Masscan, 2024. [Online]. Available: https://github.com/robertdavidgraham/masscan

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