In the computing landscape of the modern world, our devices and
systems, including PCs, servers, industrial control systems, and
smart/embedded devices, are increasingly relying on programs for which
the source code is unavailable to end users, security analysts, and
even manufacturers – termed “binary programs”. Oftentimes, binary
programs are not fully secure, and through these devices and systems,
vulnerabilities in binaries may have a broad impact on society.
Because of the intrinsic complexity of programs, the discovery and
mitigation of vulnerabilities in binaries is generally viewed as a
difficult task. It is only more difficult due to the loss of
information, especially semantics, through compilation and
In this talk, I will present my research on improving the discovery
and mitigation of vulnerabilities in binaries without requiring source
code. I approach this goal from different angles. I will first discuss
improvements on traditional vulnerability discovery techniques, such
as fuzz testing, by complimenting them with assistance from either
symbolic execution engines or intelligence from non-expert humans. I
will then showcase a novel technique for static binary rewriting with
extremely low overhead, which greatly reduces the performance impact
of vulnerability mitigation and program hardening on binaries. These
techniques are built upon the angr binary analysis platform, which I
co-founded and maintain to help foster the future of binary analysis.
Ruoyu (Fish) Wang is a Ph.D. candidate in the SecLab of the Department
of Computer Science at the University of California, Santa Barbara,
being advised by Prof. Giovanni Vigna and Prof. Christopher Kruegel.
His research focuses on system security, especially on automated
binary program analysis and reverse engineering of software. He is the
co-founder and a core developer of the binary analysis platform, angr.
He is a core member of the CTF team Shellphish and the CGC team
Shellphish CGC, with whom he won the third place in the Final Event of
the DARPA Cyber Grand Challenge in 2016.