Survey of Automated Vulnerability Detection and Exploit Generation Techniques in Cyber Reasoning Systems
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Abstract (arXiv)
Software is everywhere, from mission critical systems such as industrial power stations, pacemakers and even household appliances. This growing dependence on technology and the increasing complexity software has serious security implications as it means we are potentially surrounded by software that contain exploitable vulnerabilities. These challenges have made binary analysis an important area of research in computer science and has emphasized the need for building automated analysis systems that can operate at scale, speed and efficacy; all while performing with the skill of a human expert. Though great progress has been made in this area of research, there remains limitations and open challenges to be addressed. Recognizing this need, DARPA sponsored the Cyber Grand Challenge (CGC), a competition to showcase the current state of the art in systems that perform; automated vulnerability detection, exploit generation and software patching. This paper is a survey of the vulnerability detection and exploit generation techniques, underlying technologies and related works of two of the winning systems Mayhem and Mechanical Phish.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2017.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2023Automated Vulnerability Detection in Source Code Using Quantum Natural Language Processingcited by 6
- 2026PoC-Adapt: Semantic-Aware Automated Vulnerability Reproduction with LLM Multi-Agents and Reinforcement Learning-Driven Adaptive Policycited by 1
- 2023Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networkscited by 0
- 2026PoC-Adapt: Semantic-Aware Automated Vulnerability Reproduction with LLM Multi-Agents and Reinforcement Learning-Driven Adaptive Policy8 Apr 2026
- 2023Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks1 Jun 2023
- 2023Automated Vulnerability Detection in Source Code Using Quantum Natural Language Processing13 Mar 2023