# SourceFinder: Finding Malware Source-Code from Publicly Available Repositories

arXiv: https://arxiv.org/abs/2005.14311 · HTML: https://mlsanity.com/paper/2005.14311/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 15 of 15)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Automated Design of Agentic Systems](https://mlsanity.com/paper/2408.08435/) (2024)
- [Instruction Tuning for Secure Code Generation](https://mlsanity.com/paper/2402.09497/) (2024)
- [Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming](https://mlsanity.com/paper/2111.07875/) (2021)
- [Weak-for-Strong: Training Weak Meta-Agent to Harness Strong Executors](https://mlsanity.com/paper/2504.04785/) (2025)
- [Codex Hacks HackerRank: Memorization Issues and a Framework for Code Synthesis Evaluation](https://mlsanity.com/paper/2212.02684/) (2022)
- [Assemblage: Automatic Binary Dataset Construction for Machine Learning](https://mlsanity.com/paper/2405.03991/) (2024)
- [SandboxEval: Towards Securing Test Environment for Untrusted Code](https://mlsanity.com/paper/2504.00018/) (2025)
- [Malware Detection and Prevention using Artificial Intelligence Techniques](https://mlsanity.com/paper/2206.12770/) (2022)
- [MALSIGHT: Exploring Malicious Source Code and Benign Pseudocode for Iterative Binary Malware Summarization](https://mlsanity.com/paper/2406.18379/) (2024)
- [Repo2Vec: A Comprehensive Embedding Approach for Determining Repository Similarity](https://mlsanity.com/paper/2107.05112/) (2021)
- [MASCOT: Analyzing Malware Evolution Through A Well-Curated Source Code Dataset](https://mlsanity.com/paper/2512.00741/) (2025)
- [Artificial Intelligence for Cybersecurity: Threats, Attacks and Mitigation](https://mlsanity.com/paper/2209.13454/) (2022)
- [MCGMark: An Encodable and Robust Online Watermark for Tracing LLM-Generated Malicious Code](https://mlsanity.com/paper/2408.01354/) (2024)
- [Security of LLM-generated Code: A Comparative Analysis](https://mlsanity.com/paper/2605.23091/) (2026)

## Abstract (arXiv)

Where can we find malware source code? This question is motivated by a real need: there is a dearth of malware source code, which impedes various types of security research. Our work is driven by the following insight: public archives, like GitHub, have a surprising number of malware repositories. Capitalizing on this opportunity, we propose, SourceFinder, a supervised-learning approach to identify repositories of malware source code efficiently. We evaluate and apply our approach using 97K repositories from GitHub. First, we show that our approach identifies malware repositories with 89% precision and 86% recall using a labeled dataset. Second, we use SourceFinder to identify 7504 malware source code repositories, which arguably constitutes the largest malware source code database. Finally, we study the fundamental properties and trends of the malware repositories and their authors. The number of such repositories appears to be growing by an order of magnitude every 4 years, and 18 malware authors seem to be "professionals" with well-established online reputation. We argue that our approach and our large repository of malware source code can be a catalyst for research studies, which are currently not possible.