Angora: Efficient Fuzzing by Principled Search

TL;DR

Angora is a novel mutation-based fuzzer that significantly improves software testing efficiency by increasing branch coverage through innovative constraint-solving techniques without relying on symbolic execution.

Contribution

It introduces scalable byte-level taint tracking, context-sensitive branch counting, gradient descent search, and input length exploration to enhance fuzzing performance.

Findings

Found almost all injected bugs in LAVA-M dataset

Detected more bugs than existing fuzzers in multiple programs

Achieved higher branch coverage and bug detection rates

Abstract

Fuzzing is a popular technique for finding software bugs. However, the performance of the state-of-the-art fuzzers leaves a lot to be desired. Fuzzers based on symbolic execution produce quality inputs but run slow, while fuzzers based on random mutation run fast but have difficulty producing quality inputs. We propose Angora, a new mutation-based fuzzer that outperforms the state-of-the-art fuzzers by a wide margin. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution. To solve path constraints efficiently, we introduce several key techniques: scalable byte-level taint tracking, context-sensitive branch count, search based on gradient descent, and input length exploration. On the LAVA-M data set, Angora found almost all the injected bugs, found more bugs than any other fuzzer that we compared with, and found eight times as many bugs as the second-best fuzzer in the program who. Angora also found 103 bugs that the LAVA authors injected but could not trigger. We also tested Angora on eight popular, mature open source programs. Angora found 6, 52, 29, 40 and 48 new bugs in file, jhead, nm, objdump and size, respectively. We measured the coverage of Angora and evaluated how its key techniques contribute to its impressive performance.