AFL++ : Combining Incremental Steps of Fuzzing Research

By Andrea Fioraldi, Dominik Christian Maier, H. Eißfeldt, Marc Heuse. WOOT @ USENIX Security Symposium, 2020

911 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ff4804de2b9db68a052d7113cae41ef2122a1c51

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yinlin Deng
2. Chun Xia
3. Lingming Zhang
4. Haoran Peng
5. Chenyuan Yang
6. Angela Fan
7. Jiawei Liu
8. Adam Suma

Citations in the corpus

• Large Language Model guided Protocol Fuzzing (2024, unknown): “Mutation-based fuzzers [47], [9], [37], [31], [21], [40], [4] generate new inputs by randomly mutating existing seeds selected from a corpus of seed inputs and utilize coverage information to systematically evolve this corpus.”
• Large Language Models Are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models (2022, methodology): “We compare our default fitness function against its variants, as well as random selection (Random) and a simplistic coverage guided [18, 35, 42, 49] (Coverage) baseline in Table 4.”