cside
c/side: The Client-Side Platform for Cybersecurity, Compliance, and Privacy
Monitoring third-party scripts effectively eliminates uncertainty, ensuring that you are always aware of what is being delivered to your users' browsers, while also enhancing script performance by up to 30%. The unchecked presence of these scripts in users' browsers can lead to significant issues when things go awry, resulting in adverse publicity, potential legal actions, and claims for damages stemming from security breaches. Compliance with PCI DSS 4.0.1, particularly sections 6.4.3 and 11.6.1, requires that organizations handling cardholder data implement tamper-detection measures by March 31, 2025, to help prevent attacks by notifying stakeholders of unauthorized modifications to HTTP headers and payment information. c/side stands out as the sole fully autonomous detection solution dedicated to evaluating third-party scripts, moving beyond reliance on merely threat feed intelligence or easily bypassed detections. By leveraging historical data and artificial intelligence, c/side meticulously analyzes the payloads and behaviors of scripts, ensuring a proactive stance against emerging threats. Our continuous monitoring of numerous sites allows us to stay ahead of new attack vectors, as we process all scripts to refine and enhance our detection capabilities. This comprehensive approach not only safeguards your digital environment but also instills greater confidence in the security of third-party integrations.
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Parasoft
Parasoft's mission is to provide automated testing solutions and expertise that empower organizations to expedite delivery of safe and reliable software.
A powerful unified C and C++ test automation solution for static analysis, unit testing and structural code coverage, Parasoft C/C++test helps satisfy compliance with industry functional safety and security requirements for embedded software systems.
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FuzzDB
FuzzDB was developed to enhance the chances of identifying security vulnerabilities in applications through dynamic testing methods. As the first and most extensive open repository of fault injection patterns, along with predictable resource locations and regex for server response matching, it serves as an invaluable resource. This comprehensive database includes detailed lists of attack payload primitives aimed at fault injection testing. The patterns are organized by type of attack and, where applicable, by the platform, and they are known to lead to vulnerabilities such as OS command injection, directory listings, directory traversals, source code exposure, file upload bypass, authentication bypass, cross-site scripting (XSS), HTTP header CRLF injections, SQL injection, NoSQL injection, and several others. For instance, FuzzDB identifies 56 patterns that might be interpreted as a null byte, in addition to offering lists of frequently used methods and name-value pairs that can activate debugging modes. Furthermore, the resource continuously evolves as it incorporates new findings and community contributions to stay relevant against emerging threats.
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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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