american fuzzy lop Description
American fuzzy lop, a security-oriented fuzzer, uses a novel form of compile-time tooling and genetic algorithms to discover clean test cases that trigger internal states within the binary. This improves the functional coverage of the fuzzed codes. The compact corpora generated by the tool can also be used to seed other, more resource-intensive or labor-intensive testing regimes in the future. Afl-fuzz, in comparison to other instrumented fuzzers, is designed to be practical. It has a modest overhead, uses highly effective fuzzing techniques and effort minimization tricks. It requires little configuration and handles complex real-world use-cases, such as common image parsing and file compression libraries. It's an instrumentation-guided genetic fuzzer capable of synthesizing complex file semantics in a wide range of non-trivial targets.
american fuzzy lop Alternatives
go-fuzz
Go-fuzz provides coverage-guided fuzzing for testing Go packages. Fuzzing is most useful for packages that parse binary and text inputs. It is also useful to harden systems that parse inputs that are potentially malicious (anything that is accepted over a LAN). Go Modules are now supported by go-fuzz. Please file an issue if you encounter a module problem. Data is a randomly generated input by go-fuzz. Note that it is usually invalid. The function must return 0 if no input should be added to the corpus, but the fuzzer must increase the priority. The fuzz function has to be in a package go-fuzz is able to import. This means that the code you wish to test cannot be in package main. However, fuzzing internal packages can be done.
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afl-unicorn
Afl-unicorn allows you to fuzz any binary code that can be emulated using Unicorn Engine. Afl-unicorn can fuzz any binary that can be emulated by Unicorn Engine. Unicorn Mode implements the block-edge instrumentation normally done by AFL's QEMU Mode into Unicorn Engine. AFL will basically use block coverage data from any emulated code to drive its input. The idea revolves around a Unicorn test harness that is constructed correctly. The Unicorn-based testing harness loads the target binary code, sets the initial state and loads data mutated by AFL. The test harness emulates the binary code of the target and, if a crash or an error occurs, it will send a signal. AFL will perform all its usual tasks, but is actually fuzzing the emulated binary code. It was only tested on Ubuntu 16.04 LTS but should work with any OS that can run both AFL and Unicorn.
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Defensics Fuzz Testing
Defensics Fuzz Testing, a comprehensive and versatile automated black box fuzzer, allows organizations to discover and remediate software security flaws efficiently and effectively. The generational fuzzer takes a targeted, intelligent approach to negative testing. Users can create their own test cases using advanced file and protocol template fuzzers. The SDK allows users to create their own test cases using the Defensics Framework. Defensics runs as a black-box fuzzer. It does not require source code. Defensics allows users to secure their cyber supply chains, ensuring interoperability, robustness and quality of software and devices, before introducing them in IT or lab environments. Defensics is compatible with any development workflow in a traditional SDL environment or a CI environment. Its API and export capabilities allow it to integrate with surrounding technology, making it a plug-and-play fuzzer.
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LibFuzzer
LibFuzzer, a coverage-guided evolutionary fuzzing tool, is a fuzzing engine that works in the background. LibFuzzer links with the library being tested and feeds fuzzed data to the library through a specific fuzzing target function. The fuzzer tracks the code coverage and generates mutations based on the input data to maximize it. SanitizerCoverage, an instrumentation of LLVM, provides code coverage information. LibFuzzer will still be fully supported, in that important bugs are fixed. To use libFuzzer with a library, you must first implement a fuzz-target. This is a function which accepts an array and performs something interesting using the API being tested. This fuzz target is not dependent on libFuzzer, so it can be used with other fuzzing engine like AFL or Radamsa.
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Pricing
Pricing Starts At:
Free
Free Version:
Yes
Integrations
Company Details
Company:
Google
Headquarters:
United States
Website:
github.com/google/AFL
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Product Details
Platforms
Mac
Linux
Type of Training
Documentation
Customer Support
Online
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