American fuzzy lop (AFL) fuzzer
Vulnerability researchers can use American Fuzzy Lop (AFL), an evolutionary fuzzer, to find problems in their software during testing. As it is a critical component of the current vulnerabilities research, fuzzing is a crucial technique to know about. Fuzzing is the most historically effective appro...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/156531 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Vulnerability researchers can use American Fuzzy Lop (AFL), an evolutionary fuzzer, to find problems in their software during testing. As it is a critical component of the current vulnerabilities research, fuzzing is a crucial technique to know about. Fuzzing is the most historically effective approach for identifying flaws, according to an informal poll of vulnerability researchers. Improved fuzzers can help researchers to identify new flaws in software that is critical. Hence, greybox fuzzing is the most solid and basically strong strategy for mechanized programming testing. Regardless, a larger part of greybox fuzzers are not compelling in coordinated fluffing, for instance, towards confounded patches, just as towards dubious and basic destinations. To beat these impediments of greybox fuzzers, Directed Greybox Fuzzing (DGF) approaches were as of late proposed. Current DGFs are strong and productive methodologies that can rival Coverage-Based Fuzzers. In any case, DGFs disregard to achieve dependability among convenience and capability, and irregular transformations make it difficult to deal with complex ways. |
|---|