mlsanityData to 7 Oct 2026

An Observational Investigation of Reverse Engineers' Processes

arXiv 1912.00317 ↗First posted 1 Dec 2019cs.CR cs.HCcited by 20

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Abstract (arXiv)

Reverse engineering is a complex process essential to software-security tasks such as vulnerability discovery and malware analysis. Significant research and engineering effort has gone into developing tools to support reverse engineers. However, little work has been done to understand the way reverse engineers think when analyzing programs, leaving tool developers to make interface design decisions based only on intuition. This paper takes a first step toward a better understanding of reverse engineers' processes, with the goal of producing insights for improving interaction design for reverse engineering tools. We present the results of a semi-structured, observational interview study of reverse engineers (N=16). Each observation investigated the questions reverse engineers ask as they probe a program, how they answer these questions, and the decisions they make throughout the reverse engineering process. From the interview responses, we distill a model of the reverse engineering process, divided into three phases: overview, sub-component scanning, and focused experimentation. Each analysis phase's results feed the next as reverse engineers' mental representations become more concrete. We find that reverse engineers typically use static methods in the first two phases, but dynamic methods in the final phase, with experience playing large, but varying, roles in each phase. % and the role of experience varies between phases. Based on these results, we provide five interaction design guidelines for reverse engineering tools.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Dec 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 1; 2024: 3; 2025: 3; 2026: 9.

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  1. 2021MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labelscited by 17
  2. 2023Binary Code Summarization: Benchmarking ChatGPT/GPT-4 and Other Large Language Modelscited by 13
  3. 2021XFL: Naming Functions in Binaries with Extreme Multi-label Learningcited by 5
  4. 2022Self-Supervised Vision Transformers for Malware Detectioncited by 5
  5. 2024BLens: Contrastive Captioning of Binary Functions using Ensemble Embeddingcited by 3
  6. 2024MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learningcited by 3
  7. 2024Living off the Analyst: Harvesting Features from Yara Rules for Malware Detectioncited by 3
  8. 2026HELIOS: Hierarchical Graph Abstraction for Structure-Aware LLM Decompilationcited by 3
  9. 2025FidelityGPT: Correcting Decompilation Distortions with Retrieval Augmented Generationcited by 2
  10. 2026Identifying Adversary Tactics and Techniques in Malware Binaries with an LLM Agentcited by 2
  11. 2026Cybersecurity is the True Frontier for Generative AI Success or Failurecited by 2
  12. 2022Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detectioncited by 1
8 more
  1. 2025Signal-Based Malware Classification Using 1D CNNscited by 1
  2. 2025AGNOMIN -- Architecture Agnostic Multi-Label Function Name Predictioncited by 1
  3. 2026EntailLLM: Verifying LLM-Generated Vulnerability Discovery Paths with Domain Knowledge via Logic Programmingcited by 1
  4. 2026The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmarkcited by 1
  5. 2026Hamm-Grams: An Algorithm for Mining Regular Expressions of Bytescited by 0
  6. 2026AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigationcited by 0
  7. 2026Reveree: Diagnosing LLM Reverse-Engineering Agentscited by 0
  8. 2026When LLM Decompilers Recompile More and Preserve Lesscited by 0