Efficient and Scalable Bug-Bounty Programs

Funded in part by the National Science Foundation under Award CNS-1850510

Aron Laszka
Aron Laszka
Taha Eghtesad
Taha Eghtesad
Afiya Ayman
Afiya Ayman
Amutheezan Sivagnanam
Amutheezan Sivagnanam
Soodeh Atefi
Soodeh Atefi
,
Shanto Roy
projects

Many organizations and companies have recently chosen to use so-called bug-bounty programs (also known as vulnerability reward programs), which allow outside security experts to evaluate the security of an organization’s products and services and to report security vulnerabilities in exchange for rewards. Bug-bounty programs provide unique benefits by allowing organizations to publicly signal their commitment to security and to harness the diverse expertise of thousands of security experts in an affordable way. Despite their rapidly growing popularity, bug-bounty programs are not well understood and can be mismanaged. As a result, bug bounty programs can waste substantial resources and they rarely live up to their potential to improve cybersecurity. This project will significantly improve the efficiency of bug-bounty programs by collecting and publishing comprehensive datasets on the bug-bounty ecosystem, by establishing a sound theory of bug-bounty programs, and by providing practical recommendations for organizations and regulators. The project will directly benefit organizations and companies by enabling them to manage bug-bounty programs more efficiently, which will allow them to eliminate security vulnerabilities at a lower cost; and it will also benefit users by improving the security of software products and services.

This project comprises four research thrusts. The first thrust will build a dataset that captures the entire bug-bounty ecosystem by collecting activity data and rule descriptions from public bug-bounty programs, conducting interviews and focus-group studies with experts who regularly participate in programs, collecting social-media posts, and incorporating existing datasets, such as vulnerability databases. The second thrust will analyze this dataset to understand the ecosystem and its actors’ incentives and actions, and to characterize processes, such as discovering and reporting vulnerabilities and rewarding reports. To code textual data, the project will first develop a terminology and taxonomy of bug-bounty related concepts. Driven by the results of the data analysis, the third thrust will develop a formal model of the bug-bounty ecosystem, which will incorporate technological processes as well as behavioral and economic incentives. To capture all aspects of such a complex ecosystem, the project will employ models and techniques from multiple disciplines and areas, including economics and cybersecurity. Building on this model, the fourth thrust will propose and evaluate novel approaches for improving the efficiency and scalability of bug-bounty programs. The project will consider policies for individual bug-bounty programs, such as rules for rewarding vulnerability reports, as well as mechanisms for regulating bug-bounty programs and for instigating coordination between them.

Team

Aron Laszka
Associate Professor

Aron Laszka is an Associate Professor in the College of Information Sciences and Technology at the Pennsylvania State University. Previously, he was an Assistant Professor at the University of Houston, a Research Assistant Professor at Vanderbilt University, and a Postdoctoral Scholar at the University of California, Berkeley.

His research interests revolve around artificial intelligence and cyber-physical systems, focusing on machine learning for decision making and optimization, with applications in smart and connected communities, critical infrastructure, and cybersecurity.

His research is funded by the National Science Foundation, the Department of Energy, the Department of Transportation, and other agencies.

Taha Eghtesad
Ph.D. 2026
Taha Eghtesad joined the Applied Artificial Intelligence Lab as a Ph.D. student in Fall 2018 and received his Ph.D. in 2026 under the supervision of Dr. Aron Laszka. He received a B.S. degree in Computer Engineering, with a minor in Software Engineering, from Shahid Beheshti University, Tehran, Iran. His research lies at the intersection of machine learning, game theory, cyber-physical systems, and systems security. In particular, his Ph.D. research focused on using artificial intelligence and machine learning to make cyber-physical systems more secure.
Afiya Ayman
Ph.D. 2025
Afiya Ayman joined the Applied Artificial Intelligence Lab as a Ph.D. student in January 2019. Her research interest is in contemporary computational sciences, including and not limited to Data Science, Machine Learning, and Cyber Security. She earned her bachelor’s degree in Computer Science & Engineering from Chittagong University of Engineering & Technology, Bangladesh. Currently, her research focuses on the application of neural networks and machine learning to restructure the operational planning of a regional transportation authority to be more energy-efficient.
Amutheezan Sivagnanam
Ph.D. 2025
Amutheezan joined the Applied Artificial Intelligence Lab as a Ph.D. student in fall 2019. His research interest is in Machine Learning and Data Science. Previously, he graduated with a Bachelors’s Degree in Computer Science and Engineering from the University of Moratuwa, Sri Lanka in 2017. He worked as an intern at WSO2 Lanka (PVT) Ltd., as part of the Data Analytics Team in 2016. He also worked as a software engineer in MillenniumIT Software PVT Ltd. from 2018 to 2019. He has previously worked on a machine learning project on semi-supervised based sentiment analysis of microblogging platforms and on a data analytics project on monitoring flows of HL7 messages. Currently, his research work focuses on optimizing bus-route scheduling and minimizing energy costs.
Soodeh Atefi
Ph.D. 2023
Soodeh joined the Resilient Networks and Systems Lab as a Ph.D. student in spring 2021. Her research interests include data science and machine learning. She defended her Ph.D. dissertation in 2023.

Publications