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What is Jazzer?
Jazzer, developed by Code Intelligence, is a coverage-guided fuzzer specifically designed for the JVM platform that functions within the process. Taking cues from libFuzzer, it integrates several sophisticated mutation capabilities enhanced by instrumentation tailored for the JVM ecosystem. Users have the option to engage with Jazzer's autofuzz mode through Docker, which automatically generates arguments for designated Java functions and detects as well as reports any anomalies or security issues that occur. Furthermore, users can access the standalone Jazzer binary from GitHub's release archives, which launches its own JVM optimized for fuzzing operations. This adaptability enables developers to rigorously assess their applications for durability against a variety of edge cases, ensuring a more secure software environment. By utilizing Jazzer, teams can enhance their testing strategies and improve overall code quality.
What is Atheris?
Atheris operates as a fuzzing engine tailored for Python, specifically employing a coverage-guided approach, and it extends its functionality to accommodate native extensions built for CPython. Leveraging libFuzzer as its underlying framework, Atheris proves particularly adept at uncovering additional bugs within native code during fuzzing processes. It is compatible with both 32-bit and 64-bit Linux platforms, as well as Mac OS X, and supports Python versions from 3.6 to 3.10. While Atheris integrates libFuzzer, which makes it well-suited for fuzzing Python applications, users focusing on native extensions might need to compile the tool from its source code to align the libFuzzer version included with Atheris with their installed Clang version. Given that Atheris relies on libFuzzer, which is bundled with Clang, users operating on Apple Clang must install an alternative version of LLVM, as the standard version does not come with libFuzzer. Atheris utilizes a coverage-guided, mutation-based fuzzing strategy, which streamlines the configuration process, eliminating the need for a grammar definition for input generation. However, this approach can lead to complications when generating inputs for code that manages complex data structures. Therefore, users must carefully consider the trade-offs between the simplicity of setup and the challenges associated with handling intricate input types, as these factors can significantly influence the effectiveness of their fuzzing efforts. Ultimately, the decision to use Atheris will hinge on the specific requirements and complexities of the project at hand.
API Availability
API Availability
Has API
Pricing Information
Free
Free Version
Pricing Information
Free
Free Version
Supported Platforms
Windows
Mac
Linux
Supported Platforms
Mac
Linux
Customer Service / Support
Web-Based Support
Customer Service / Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Company Facts
Organization Name
Code Intelligence
Company Location
Germany
Company Website
github.com/CodeIntelligenceTesting/jazzer
Company Facts
Organization Name
Company Website
github.com/google/atheris
Categories and Features
Fuzz Testing
Not specified
Categories and Features
Fuzz Testing
Not specified