AI-assisted reverse engineering
AI-assisted reverse engineering (AIARE) is a branch of computer science that leverages artificial intelligence (AI), notably machine learning (ML) strategies, to augment and automate the process of reverse engineering. The latter involves breaking down a product, system, or process to comprehend its structure, design, and functionality. AIARE was primarily introduced in the early years of the 21st century, witnessing substantial advancements from the mid-2010s onwards.
Overview
[edit]Conventionally, reverse engineering is conducted by specialists who dismantle a system to grasp its working principles, often for the purposes of reproduction, modification, enhancement of compatibility, or forensic examination. This method, while efficient, can be laborious and time-intensive, particularly when dealing with intricate software or hardware systems.[1][2][3]
AIARE integrates machine learning algorithms to either partially automate or augment this process.[4][5] It is capable of detecting patterns, relationships, structures, and potential vulnerabilities within the analyzed system, frequently surpassing human experts in speed and accuracy. This has rendered AIARE a critical tool in numerous fields, including cybersecurity, software development, and hardware design and analysis.[6]
Techniques
[edit]AIARE encompasses several AI methodologies:
Supervised learning
[edit]Supervised learning employs tagged data to train models to recognize system components, their operations, and their interconnections. This method is particularly helpful in software analysis to discover vulnerabilities or enhance compatibility.[3][7][8]
Unsupervised learning
[edit]Unsupervised learning is utilized to detect concealed patterns and structures in untagged data. It proves beneficial in comprehending complex systems where there's no evident labeling or mapping of components.[1][9]
Reinforcement learning
[edit]Reinforcement learning is employed to build models that progressively refine their system understanding through a process of trial and error. This method is often implemented when deciphering a system's functionality under various circumstances or configurations.[1][5]
Deep learning
[edit]Deep learning is employed for analysis of high-dimensional data. For instance, deep learning techniques can aid in examining the layout and connections of integrated circuits (ICs), substantially reducing the manual effort required for reverse engineering.[3][4]
Benefits
[edit]Usable Security
[edit]AIARE expands usable security as reverse engineering is traditionally slow and highly specialized as it produces dense, low-level information (usually in Assembly or C) when using tools like Ghidra. The use of multiple different methods to interface with models today (such as through chat bots like ChatGPT) greatly reduces the barrier to entry by providing a clear way to interact with the user and even providing meaningful decompiled source code.[10] In addition, either done automatically or through prompt engineering, a model is capable of producing a high-level summary and explanation of its reverse engineering efforts in human-readable form that doesn't require much knowledge on code.[11]
Speedup
[edit]AIARE is capable of processing data much faster than humans, providing a boost in speed when analyzing said data. In the context of computer security, this can greatly speed up incident management or response and malware detection as AIARE can be automated to drastically reduce the manual effort usually associated with reverse engineering.[12][13]
Limitations
[edit]In an effort to improve readability for reverse engineering, AI-generated code may introduce erroneous bugs not present in the source. This compromises the correctness of the code if not carefully validated and will throw off reverse engineering efforts.[14] Additionally, AIARE's weakness in zero-shot prompting makes gathering accurate data without reference data in the prompt more inconsistent, thus requiring a user to provide some quality data of their own that hurts its usability.[15]
References
[edit]- ^ a b c Neukart, Florian (2017). Reverse engineering the mind: consciously acting machines and accelerated evolution. AutoUni – Schriftenreihe. Wiesbaden: Springer. ISBN 978-3-658-16175-0.
- ^ Bayern, Shawn (2022-12-13), "Reverse engineering (by) artificial intelligence", Research Handbook on Intellectual Property and Artificial Intelligence, Edward Elgar Publishing, pp. 391–404, doi:10.4337/9781800881907.00029, ISBN 978-1-80088-190-7, retrieved 2023-07-06
- ^ a b c Ethier, Stephen P. (2023). Using Functional Genomics and Artificial Intelligence to Reverse Engineer Human Cancer Cells. Cambridge Scholars. ISBN 978-1-5275-9230-8.
- ^ a b Eilam, Eldad (2005). Reversing: secrets of reverse engineering (Nachdr. ed.). Indianapolis, Ind: Wiley. ISBN 978-0-7645-7481-8.
- ^ a b Horváth, Imre; Technische Universiteit Delft; Budapesti Műszaki és Gazdaságtudományi Egyetem, eds. (2014). Tools and methods of competitive engineering: proceedings of the Tenth International Symposium on Tools and Methods of Competitive Engineering - TMCE 2014, May 19 - 23, Budapest, Hungary. Delft: Faculty of Industrial Design Engineering, Delft University of Technology. ISBN 978-94-6186-177-1.
- ^ Eilam, Eldad (2005). Reversing: secrets of reverse engineering (Nachdr. ed.). Indianapolis, Ind: Wiley. ISBN 978-0-7645-7481-8.
- ^ Alexandru C., Telea (2012). Reverse Engineering - Recent Advances and Applications. InTech. ISBN 978-9535101581.
- ^ Tonella, Paolo; Torchiano, Marco; Du Bois, Bart; Systä, Tarja (2007-09-20). "Empirical studies in reverse engineering: state of the art and future trends". Empirical Software Engineering. 12 (5): 551–571. doi:10.1007/s10664-007-9037-5. ISSN 1382-3256.
- ^ Abbott, Ryan, ed. (2022). Research handbook on intellectual property and artificial intelligence. Research handbooks in intellectual property. Cheltenham Northampton, MA: Edward Elgar Publishing. ISBN 978-1-80088-189-1.
- ^ Tan, Hanzhuo; Luo, Qi; Li, Jing; Zhang, Yuqun (2024). "LLM4Decompile: Decompiling Binary Code with Large Language Models". Association for Computational Linguistics: 3473–3487. doi:10.18653/v1/2024.emnlp-main.203.
{{cite journal}}: Cite journal requires|journal=(help) - ^ Pearce, Hammond; Tan, Benjamin; Krishnamurthy, Prashanth; Khorrami, Farshad; Karri, Ramesh; Dolan-Gavitt, Brendan (2022-02-02), Pop Quiz! Can a Large Language Model Help With Reverse Engineering?, arXiv, doi:10.48550/arXiv.2202.01142, arXiv:2202.01142, retrieved 2025-11-18
- ^ Xie, Danning; Zhang, Zhuo; Jiang, Nan; Xu, Xiangzhe; Tan, Lin; Zhang, Xiangyu (2024-12-09). "ReSym: Harnessing LLMs to Recover Variable and Data Structure Symbols from Stripped Binaries". Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security. CCS '24. New York, NY, USA: Association for Computing Machinery: 4554–4568. doi:10.1145/3658644.3670340. ISBN 979-8-4007-0636-3.
- ^ Chen, Guoqiang; Sun, Huiqi; Liu, Daguang; Wang, Zhiqi; Wang, Qiang; Yin, Bin; Liu, Lu; Ying, Lingyun (2025-05-22), ReCopilot: Reverse Engineering Copilot in Binary Analysis, arXiv, doi:10.48550/arXiv.2505.16366, arXiv:2505.16366, retrieved 2025-11-18
- ^ Zou, Muqi; Cai, Hongyu; Wu, Hongwei; Basque, Zion Leonahenahe; Khan, Arslan; Celik, Berkay; Dave; Tian; Bianchi, Antonio (2025-08-15), D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning, arXiv, doi:10.48550/arXiv.2506.10125, arXiv:2506.10125, retrieved 2025-11-18
- ^ Hu, Xinyu; Fu, Zhiwei; Xie, Shaocong; Ding, Steven H. H.; Charland, Philippe (2025-09-26), SoK: Potentials and Challenges of Large Language Models for Reverse Engineering, arXiv, doi:10.48550/arXiv.2509.21821, arXiv:2509.21821, retrieved 2025-11-18