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USENIX Security2019顶会

Stack Overflow Considered Helpful! Deep Learning Security Nudges Towards Stronger Cryptography

Felix Fischer, Huang Xiao, Ching-yu Kao, Yannick Stachelscheid, Benjamin Johnson, Danial Razar, Paul Fawkesley, Nat Buckley, Konstantin Böttinger, Paul Muntean, Jens Grossklags

出版方
2019年份
44被引次数
13顶会引用

摘要

Stack Overflow is the most popular discussion platform for software developers. However, recent research identified a large amount of insecure encryption code in production systems that has been inspired by examples given on Stack Overflow. By copying and pasting functional code, developers introduced exploitable software vulnerabilities into security-sensitive high-profile applications installed by millions of users every day. Proposed mitigations of this problem suffer from usability flaws and push developers to continue shopping for code examples on Stack Overflow once again. This motivates us to fight the proliferation of insecure code directly at the root before it even reaches the clipboard. By viewing Stack Overflow as a market, implementation of cryptography becomes a decision-making problem. In this context, our goal is to simplify the selection of helpful and secure examples. More specifically, we focus on supporting software developers in making better decisions on Stack Overflow by applying nudges, a concept borrowed from behavioral economics and psychology. This approach is motivated by one of our key findings: For 99.37% of insecure code examples on Stack Overflow, similar alternatives are available that serve the same use case and provide strong cryptography. Our system design that modifies Stack Overflow is based on several nudges that are controlled by a deep neural network. It learns a representation for cryptographic API usage patterns and classification of their security, achieving average AUC-ROC of 0.992. With a user study, we demonstrate that nudge-based security advice significantly helps tackling the most popular and error-prone cryptographic use cases in Android.

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