Google OSS-Fuzz Description
OSS-Fuzz provides ongoing fuzz testing for open source applications, a method renowned for identifying programming flaws. Such flaws, including buffer overflow vulnerabilities, can pose significant security risks. Through the implementation of guided in-process fuzzing on Chrome components, Google has discovered thousands of security weaknesses and stability issues, and now aims to extend this beneficial service to the open source community. The primary objective of OSS-Fuzz is to enhance the security and stability of frequently used open source software by integrating advanced fuzzing methodologies with a scalable and distributed framework. For projects that are ineligible for OSS-Fuzz, there are alternatives available, such as running personal instances of ClusterFuzz or ClusterFuzzLite. At present, OSS-Fuzz is compatible with languages including C/C++, Rust, Go, Python, and Java/JVM, with the possibility of supporting additional languages that are compatible with LLVM. Furthermore, OSS-Fuzz facilitates fuzzing for both x86_64 and i386 architecture builds, ensuring a broad range of applications can benefit from this innovative testing approach. With this initiative, we hope to build a safer software ecosystem for all users.
Google OSS-Fuzz Alternatives
Anaconda is an AI-native development platform that gives teams a governed path from early experimentation to production AI deployment. The platform is built around trusted open-source packages, secure Python package management, controlled environments, and production-grade orchestration. Anaconda helps organizations avoid broken environments, dependency conflicts, security gaps, and deployment delays that can slow AI initiatives. Anaconda Core provides validated packages, automated security scanning, and intelligent dependency conflict resolution for Python and data science teams. Its platform capabilities support AI orchestration, trusted distribution, and enterprise workflows for building and scaling models. Anaconda is widely used across the AI, data science, Python, and enterprise developer communities. The company positions open source as a foundation for AI innovation and emphasizes Python as a core language for the next era of AI development. Anaconda also provides learning courses, certifications, reports, guides, professional services, documentation, and support resources. By combining package governance, environment management, open-source security, and AI development workflows, Anaconda helps teams build trusted AI systems on their own terms.
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JS7 JobScheduler, an Open Source Workload Automation System, is designed for performance and resilience. JS7 implements state-of-the-art security standards. It offers unlimited performance for parallel executions of jobs and workflows.
JS7 provides cross-platform job execution and managed file transfer. It supports complex dependencies without the need for coding. The JS7 REST-API allows automation of inventory management and job control.
JS7 can operate thousands of Agents across any platform in parallel.
Platforms
- Cloud scheduling for Docker®, OpenShift®, Kubernetes® etc.
- True multi-platform scheduling on premises, for Windows®, Linux®, AIX®, Solaris®, macOS® etc.
- Hybrid cloud and on-premises use
User Interface
- Modern GUI with no-code approach for inventory management, monitoring, and control using web browsers
- Near-real-time information provides immediate visibility to status changes, log outputs of jobs and workflows.
- Multi-client functionality, role-based access management
- OIDC authentication and LDAP integration
High Availability
- Redundancy & Resilience based on asynchronous design and autonomous Agents
- Clustering of all JS7 Products, automatic fail-over and manual switch-over
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LibFuzzer
LibFuzzer serves as an in-process, coverage-guided engine for evolutionary fuzzing. By being linked directly with the library under examination, it injects fuzzed inputs through a designated entry point, or target function, allowing it to monitor the code paths that are executed while creating variations of the input data to enhance code coverage. The coverage data is obtained through LLVM’s SanitizerCoverage instrumentation, ensuring that users have detailed insights into the testing process. Notably, LibFuzzer continues to receive support, with critical bugs addressed as they arise. To begin utilizing LibFuzzer with a library, one must first create a fuzz target—this function receives a byte array and interacts with the API being tested in a meaningful way. Importantly, this fuzz target operates independently of LibFuzzer, which facilitates its use alongside other fuzzing tools such as AFL or Radamsa, thereby providing versatility in testing strategies. Furthermore, the ability to leverage multiple fuzzing engines can lead to more robust testing outcomes and clearer insights into the library's vulnerabilities.
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go-fuzz
Go-fuzz serves as a coverage-guided fuzzing tool designed specifically for testing Go packages, making it particularly effective for those that handle intricate inputs, whether they are textual or binary in nature. This method of testing is crucial for strengthening systems that need to process data from potentially harmful sources, such as network interactions. Recently, go-fuzz has introduced initial support for fuzzing Go Modules, inviting users to report any issues they encounter with detailed descriptions. It generates random input data, which is often invalid, and the function must return a value of 1 to indicate that the fuzzer should elevate the priority of that input in future fuzzing attempts, provided that it should not be stored in the corpus, even if it uncovers new coverage; a return value of 0 signifies the opposite, while other values are reserved for future enhancements. The fuzz function is required to reside in a package that go-fuzz can recognize, meaning the code under test cannot be located within the main package, although fuzzing of internal packages is permitted. This structured approach ensures that the testing process remains efficient and focused on identifying vulnerabilities in the code.
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Pricing
Pricing Starts At:
Free
Free Version:
Yes
Integrations
Company Details
Company:
Google
Headquarters:
United States
Website:
github.com/google/oss-fuzz
Media
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Product Details
Platforms
Windows
Mac
Linux
Types of Training
Training Docs
Customer Support
Online Support
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