Sulley Description
Sulley is an extensible fuzzing engine, and fuzz testing framework. Sulley (IMHO), surpasses the capabilities of many previously published fuzzing techniques, both commercial and public domain. The framework's goal is to simplify data representation, data transmission, and instrumentation. A pure-Python, fully automated and unattended framework for fuzzing. Sulley has not only impressive data generation, but has gone a step further to include many other important aspects that a modern fuzzer should provide. Sulley keeps meticulous records and monitors the network. Sulley monitors and instruments the target's health, capable of reverting back to a known-good state using multiple methods. Sulley tracks, categorizes and detects faults. Sulley can fuzz simultaneously, increasing test speed. Sulley can automatically identify which unique sequence of test cases triggers a fault.
Sulley Alternatives
Boofuzz
Boofuzz forks and succeeds the venerable Sulley fuzzing framework. Boofuzz is a fork of the venerable Sulley fuzzing framework. It aims to be extensible, in addition to numerous bug fixes. Boofuzz, like Sulley, incorporates all of the critical elements that make up a fuzzer, such as easy and quick data creation, instrumentation and detection of failures, target reset after failure and recording of test results. Installation is much easier and supports arbitrary communication mediums. Support for serial fuzzing and UDP broadcast. Consistent, thorough and clear recording of test data. Test result CSV export and extensible instrumentation/failure detection. Boofuzz is installed as a Python Library used to create fuzzer scripts. It is highly recommended that Boofuzz be installed in a virtual environment.
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american fuzzy lop
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.
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Google ClusterFuzz
ClusterFuzz provides a scalable fuzzing system that can be used to find security and stability issues within software. Google uses ClusterFuzz as the fuzzing engine for OSS Fuzz and to fuzz all Google Products. ClusterFuzz offers many features that allow fuzzing to be seamlessly integrated into the software development process. Fully automatic bug filing and triage for different issue trackers. Supports multiple coverages-guided fuzzing engines to achieve optimal results (with ensemble fuzzing and fuzzing strategy). Statistics to analyze fuzzer performance and crash rates. Web interface for managing and viewing crashes. Support for multiple authentication providers using Firebase. Support for black-box fuzzing, test case minimization and regression finding using bisection.
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ClusterFuzz
ClusterFuzz provides a scalable fuzzing system that can be used to find security and stability issues within software. Google uses ClusterFuzz as the fuzzing engine for OSS Fuzz and to fuzz all Google Products. ClusterFuzz offers many features that allow fuzzing to be seamlessly integrated into the software development process. Fully automatic bug filing and triage for different issue trackers. Supports multiple coverages-guided fuzzing engines to achieve optimal results (with ensemble fuzzing and fuzzing strategy). Statistics to analyze fuzzer performance and crash rates. Web interface for managing and viewing crashes. Support for multiple authentication providers using Firebase. Support for black-box fuzzing, test case minimization and regression finding using bisection.
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Pricing
Pricing Starts At:
Free
Free Version:
Yes
Integrations
Company Details
Company:
OpenRCE
Website:
github.com/OpenRCE/sulley
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Product Details
Platforms
Windows
Mac
Linux
Type of Training
Documentation
Customer Support
Online
Sulley Features and Options
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