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Building a Base for Cyber-autonomy


As software becomes increasingly embedded in our daily lives, it becomes more and more critical to find the vulnerabilities in this software. Worse, since the amount and variety of this software is rapidly proliferating, manual analysis by rare, talented hackers cannot scale to keep this software safe.

My first foray into what became my dissertation work tried to address a specific class of this general problem: backdoors inserted (either due to malice or for later support and maintenance convenience) into internet-connected embedded devices, such as smart power-meters. To address the requirement of having to reason about logical bugs and to analyze enormous amounts of binary code, I created a novel combination of static and dynamic-symbolic analysis techniques. Combining this with an insight into a new way to define a backdoor, I was able to build a system that analyzed firmware of real-world devices to identify such vulnerabilities in them.

This first foray led into my main contribution: the generalization of this analysis composition in the form of a principled binary analysis framework built to enable the seamless combination of diverse program analysis techniques. This framework, angr, provides a powerful base future research from myself, my lab-mates, and researchers around the world (as the framework is fully open source). One of the early applications of the system was the identification of authentication bypass vulnerabilities in binary firmware using a combination of static analysis and dynamic symbolic execution.

Using angr, we built an autonomous program analysis system that was able to analyze, exploit, and protect binary code without any human intervention. This system, the Mechanical Phish, won third place in the Cyber Grand Challenge, a competition created by DARPA to bootstrap the development of autonomous Cyber Reasoning Systems. While the system did well, its performance in the Cyber Grand Challenge provided an insight that shaped the conclusion of my dissertation: even with the current program analysis techniques combined into a coherent Cyber Reasoning System, serious limitations still exist. In the final work of my graduate studies, I explored the careful reintegration of human assistance into our analysis automation, in a way that addresses its limitations without compromising its scalability advantage over manual analysis.

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