Not all bytes are equal: Neural byte sieve for fuzzing

Understand

Fuzzing is a popular dynamic program analysis technique used to find vulnerabilities in complex software.

• Fuzzing involves presenting a target program with crafted malicious input designed to cause crashes, buffer overflows, memory errors, and exceptions.
• Crafting malicious inputs in an efficient manner is a difficult open problem and often the best approach to generating such inputs is through applying uniform random mutations to pre-existing valid inputs (seed files).
• We present a learning technique that uses neural networks to learn patterns in the input files from past fuzzing explorations to guide future fuzzing explorations.