# The Coming Era of AlphaHacking? A Survey of Automatic Software Vulnerability Detection, Exploitation and Patching Techniques

arXiv: https://arxiv.org/abs/1805.11001 · HTML: https://mlsanity.com/paper/1805.11001/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2018.

## Built on by (most cited first, 5 of 5)

- [Neural Transfer Learning for Repairing Security Vulnerabilities in C Code](https://mlsanity.com/paper/2104.08308/) (2021)
- [Enhanced Automated Code Vulnerability Repair using Large Language Models](https://mlsanity.com/paper/2401.03741/) (2024)
- [A comparative study of neural network techniques for automatic software vulnerability detection](https://mlsanity.com/paper/2104.14978/) (2021)
- [Agent-Driven Automatic Software Improvement](https://mlsanity.com/paper/2406.16739/) (2024)
- [PatUntrack: Automated Generating Patch Examples for Issue Reports without Tracked Insecure Code](https://mlsanity.com/paper/2408.08619/) (2024)

## Abstract (arXiv)

With the success of the Cyber Grand Challenge (CGC) sponsored by DARPA, the topic of Autonomous Cyber Reasoning System (CRS) has recently attracted extensive attention from both industry and academia. Utilizing automated system to detect, exploit and patch software vulnerabilities seems so attractive because of its scalability and cost-efficiency compared with the human expert based solution. In this paper, we give an extensive survey of former representative works related to the underlying technologies of a CRS, including vulnerability detection, exploitation and patching. As an important supplement, we then review several pioneer studies that explore the potential of machine learning technologies in this field, and point out that the future development of Autonomous CRS is inseparable from machine learning.