Tim King
I joined Google in September 2015. Since 2018, I have led a small team working on static analysis for the Go programming language. Other work I have at Google includes FUDGE, a system for synthesizing C++ fuzz targets at scale, and fuzzing at scale infrastructure. My work has touched several other areas of programming languages, formal methods, and software security including: decision procedures, automated reasoning, symbolic execution, type inference, and formal verification.
I received a BS with Honors in Computer Science from Stanford University in 2008. After this, I worked with Clark Barrett on my Ph.D. at New York University until 2014. Before Google, I have worked at several research laboratories including Verimag, Bell Labs, SRI International, and MIT Lincoln Labs.
I am one of the authors of the Satisfiability Modulo Theories solver CVC4. The SMT problem is determining the satisfiability of first-order formulas modulo background theories. I am the author of its decision procedures for linear real and integer arithmetic.
I received a BS with Honors in Computer Science from Stanford University in 2008. After this, I worked with Clark Barrett on my Ph.D. at New York University until 2014. Before Google, I have worked at several research laboratories including Verimag, Bell Labs, SRI International, and MIT Lincoln Labs.
I am one of the authors of the Satisfiability Modulo Theories solver CVC4. The SMT problem is determining the satisfiability of first-order formulas modulo background theories. I am the author of its decision procedures for linear real and integer arithmetic.
Authored Publications
Sort By
FUDGE: Fuzz Driver Generation at Scale
Yaohui Chen
Markus Kusano
Caroline Lemieux
Wei Wang
Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ACM
Preview abstract
At Google we have found tens of thousands of security and robustness bugs by fuzzing C and C++ libraries. To fuzz a library, a fuzzer requires a fuzz driver—which exercises some library code—to which it can pass inputs. Unfortunately, writing fuzz drivers remains a primarily manual exercise, a major hindrance to the widespread adoption of fuzzing. In this paper, we address this major hindrance by introducing the Fudge system for automated fuzz driver generation. Fudge automatically generates fuzz driver candidates for libraries based on existing client code. We have used Fudge to generate thousands of new drivers for a wide variety of libraries. Each generated driver includes a synthesized C/C++ program and a corresponding build script, and is automatically analyzed for quality. Developers have integrated over 200 of these generated drivers into continuous fuzzing services and have committed to address reported security bugs. Further, several of these fuzz drivers have been upstreamed to open source projects and integrated into the OSS-Fuzz fuzzing infrastructure. Running these fuzz drivers has resulted in over 150 bug fixes, including the elimination of numerous exploitable security vulnerabilities.
View details
