Your mission-critical systems carry the weight of your entire organization. Protecting them shouldn't leave you guessing about hidden vulnerabilities or compliance risks.
Rocket® z/Assure™ Vulnerability Analysis Program (VAP) is a specialized mainframe security solution built to proactively scan and safeguard your most valuable environments. By identifying system-level risks before they become active threats, we partner with you to ensure your infrastructure remains locked down, resilient, and fully compliant. We understand the responsibility of managing enterprise security, and our tool gives you the exact insights you need to confidently eliminate weak points.
Key benefits for your security team:
- Identify and resolve hidden vulnerabilities with deep, automated scanning.
- Protect your mission-critical data from evolving external and internal threats.
- Streamline compliance reporting with clear, actionable security insights.
Take control of your mainframe security. Partner with Rocket Software to protect your digital foundation today.
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Source Defense is an essential element of web safety that protects data at the point where it is entered. Source Defense Platform is a simple, yet effective solution to data security and privacy compliance. It addresses threats and risks that arise from the increased use JavaScript, third party vendors, and open source code in your web properties. The Platform offers options for securing code as well as addressing an ubiquitous gap in managing third-party digital supply chains risk - controlling actions of third-party, forth-party and nth-party JavaScript that powers your website experience.
Source Defense Platform provides protection against all types of client-side security incidents, including keylogging, formjacking and digital skimming. Magecart is also protected. - by extending the web security beyond the browser to the server.
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Atheris
Atheris is a Python fuzzing engine guided by coverage, designed to test both Python code and native extensions developed for CPython. It is built on the foundation of libFuzzer, providing an effective method for identifying additional bugs when fuzzing native code. Atheris is compatible with Linux (both 32- and 64-bit) and Mac OS X, supporting Python versions ranging from 3.6 to 3.10. Featuring an integrated libFuzzer, it is well-suited for fuzzing Python applications, but when targeting native extensions, users may need to compile from source to ensure compatibility between the libFuzzer version in Atheris and their Clang installation. Since Atheris depends on libFuzzer, which is a component of Clang, users of Apple Clang will need to install a different version of LLVM, as the default does not include libFuzzer. The implementation of Atheris as a coverage-guided, mutation-based fuzzer (LibFuzzer) simplifies the setup process by eliminating the need for input grammar definition. However, this approach can complicate the generation of inputs for code that processes intricate data structures. Consequently, while Atheris offers ease of use in many scenarios, it may face challenges when dealing with more complex parsing requirements.
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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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