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). However, 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 properties of the reported vulnerabilities, such as severity or exploitability. Importantly, 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.
翻译:最近,错误赔偿方案越来越受欢迎,并成为许多组织安全文化的重要部分。错误赔偿方案使各组织能够利用外部安全专家(即虫猎人)的各类专门知识来加强其安全态势。然而,对错误赔偿方案的好处进行量化仍然难以实现,这对管理它们来说是一个重大挑战。以前的研究侧重于从报告的脆弱性数量或所报告的脆弱性的特性(例如严重性或可利用性)来衡量其益处。重要的是,除了这些内在性能外,报告的价值还取决于威胁类型外部行为者在内部专家发现和补补补时发现其脆弱性的可能性。在本论文中,我们对铬和Firefox脆弱性回报方案的数据驱动研究仍然难以实现。首先,我们用重新发现的可能性作为新的衡量标准来估计发现脆弱性的困难。我们的研究结果表明,脆弱性发现和弥补通过使威胁行为体难以找到脆弱性,因此,风险类型内部脆弱性的价值也取决于改进的机会,例如,在内部风险类型专家发现和补习用风险之前,我们发现的重要风险也是通过外部风险类型来比较这些脆弱性。我们发现的风险类型,通过利用外部行为者发现的风险类型,我们发现,通过外部风险类型来比较这些脆弱性,我们所发现的风险类型,通过外部行为者发现的风险是更大的风险,我们发现,通过外部行为者的弱点的弱点的弱点是如何发现。