
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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Astra's Pentest is a comprehensive solution for penetration testing. It includes an intelligent vulnerability scanner and in-depth manual pentesting.
The automated scanner performs 10000+ security checks, including security checks for all CVEs listed in the OWASP top 10 and SANS 25. It also conducts all required tests to comply with ISO 27001 and HIPAA.
Astra provides an interactive pentest dashboard which allows users to visualize vulnerability analysis, assign vulnerabilities to team members, collaborate with security experts, and to collaborate with security experts. The integrations with CI/CD platforms and Jira are also available if users don't wish to return to the dashboard each time they want to use it or assign a vulnerability for a team member.
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Awesome Fuzzing
Awesome Fuzzing serves as a comprehensive compilation of resources for those interested in the field of fuzzing, encompassing an array of materials such as books, both free and paid courses, videos, tools, tutorials, and vulnerable applications ideal for hands-on practice to enhance one's understanding of fuzzing and the early stages of exploit development, including root cause analysis. It features instructional videos focused on fuzzing methodologies, essential tools, and recommended practices, alongside conference presentations, tutorials, and blogs dedicated to the subject. Additionally, it includes software tools that facilitate fuzzing of applications, particularly those utilizing network protocols like HTTP, SSH, and SMTP. Users are encouraged to search for and select exploits linked to downloadable applications, where they can then recreate the exploits with their preferred fuzzer. The resource also encompasses a range of tests tailored for fuzzing engines, highlighting various well-known vulnerabilities and providing a corpus of diverse file formats to enable fuzzing across multiple targets found in the existing fuzzing literature. Ultimately, this collection aims to empower learners with the necessary knowledge and skills to effectively engage with fuzzing techniques and develop their expertise in security testing.
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OWASP WSFuzzer
Fuzz testing, commonly referred to as fuzzing, is a technique used in software testing that aims to discover implementation errors by injecting malformed or semi-malformed data in an automated way. For example, consider a scenario involving an integer variable within a program that captures a user's selection among three questions; the user's choice can be represented by the integers 0, 1, or 2, resulting in three distinct cases. Since integers are typically stored as fixed-size variables, a failure to implement the default switch case securely could lead to program crashes and various traditional security vulnerabilities. Fuzzing serves as an automated method for uncovering software implementation issues, enabling the identification of bugs when they occur. A fuzzer is a specialized tool designed to automatically inject semi-random data into the program stack, aiding in the detection of anomalies. The process of generating this data involves the use of generators, while the identification of vulnerabilities often depends on debugging tools that can analyze the program's behavior under the influence of the injected data. These generators typically utilize a mixture of established static fuzzing vectors to enhance the testing process, ultimately contributing to more robust software development practices.
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