The Benefits of Vulnerability Discovery and Bug Bounty Programs: Case Studies of Chromium and Firefox

May 2023·
Soodeh Atefi
Soodeh Atefi
Amutheezan Sivagnanam
Amutheezan Sivagnanam
Afiya Ayman
Afiya Ayman
,
Jens Grossklags
Aron Laszka
Aron Laszka
Abstract
Recently, bug-bounty programs have gained popularity and become a significant part of the security culture of many organizations. Bug-bounty programs enable organizations to enhance their security posture by harnessing the diverse expertise of crowds of external security experts (i.e., bug hunters). Nonetheless, quantifying the benefits of bug-bounty programs remains elusive, which presents a significant challenge for managing them. Previous studies focused on measuring their benefits in terms of the number of vulnerabilities reported or based on the properties of the reported vulnerabilities, such as severity or exploitability. However, beyond these inherent properties, the value of a report also depends on the probability that the vulnerability would be discovered by a threat actor before an internal expert could discover and patch it. In this paper, we present a data-driven study of the Chromium and Firefox vulnerability-reward programs. First, we estimate the difficulty of discovering a vulnerability using the probability of rediscovery as a novel metric. Our findings show that vulnerability discovery and patching provide clear benefits by making it difficult for threat actors to find vulnerabilities; however, we also identify opportunities for improvement, such as incentivizing bug hunters to focus more on development releases. Second, we compare the types of vulnerabilities that are discovered internally vs. externally and those that are exploited by threat actors. We observe significant differences between vulnerabilities found by external bug hunters, internal security teams, and external threat actors, which indicates that bug-bounty programs provide an important benefit by complementing the expertise of internal teams, but also that external hunters should be incentivized more to focus on the types of vulnerabilities that are likely to be exploited by threat actors.
Type
Venue
The Web Conference (WWW 2023), 2209–2219
publications

Acceptance Rate: 19%

Authors

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.
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.
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.
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.