Airlock Digital provides application control and allowlisting, used by organizations worldwide to protect against ransomware, malware and other cyber threats. Our deny by default solution enables customers to run only the applications and files they trust, with all others blocked from executing. This approach minimizes attack surfaces and helps organizations align their cybersecurity strategies with government frameworks and standards. By securing endpoints running legacy and new versions of Windows, macOS and Linux, we extend protection across IT and operational technology environments. Airlock Digital delivers endpoint protection to financial services, government, healthcare, manufacturing and other industry organizations of all sizes.
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LM-Kit.NET serves as a comprehensive toolkit tailored for the seamless incorporation of generative AI into .NET applications, fully compatible with Windows, Linux, and macOS systems. This versatile platform empowers your C# and VB.NET projects, facilitating the development and management of dynamic AI agents with ease.
Utilize efficient Small Language Models for on-device inference, which effectively lowers computational demands, minimizes latency, and enhances security by processing information locally. Discover the advantages of Retrieval-Augmented Generation (RAG) that improve both accuracy and relevance, while sophisticated AI agents streamline complex tasks and expedite the development process.
With native SDKs that guarantee smooth integration and optimal performance across various platforms, LM-Kit.NET also offers extensive support for custom AI agent creation and multi-agent orchestration. This toolkit simplifies the stages of prototyping, deployment, and scaling, enabling you to create intelligent, rapid, and secure solutions that are relied upon by industry professionals globally, fostering innovation and efficiency in every project.
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ToothPicker
ToothPicker is an advanced in-process, coverage-guided fuzzer that is specifically tailored for iOS, with a primary focus on the Bluetooth daemon and a variety of Bluetooth protocols. Built on the FRIDA framework, this tool can be customized to operate on any platform that supports FRIDA. Additionally, the repository includes an over-the-air fuzzer that provides a practical example of fuzzing Apple's MagicPairing protocol via InternalBlue. It also comes with the ReplayCrashFile script, which helps verify any crashes detected by the in-process fuzzer. This straightforward fuzzer works by altering bits and bytes in inactive connections and, while it does not incorporate coverage or injection methods, it effectively demonstrates its functionality in a stateful manner. Only requiring Python and Frida to run, it dispenses with the need for further modules or installations. Since it is based on the frizzer codebase, it is recommended to create a virtual Python environment to ensure optimal performance with frizzer. The introduction of the iPhone XR/Xs has brought about the implementation of the PAC (Pointer Authentication Code) feature, highlighting the importance of continuously evolving fuzzing tools like ToothPicker to align with the changing landscape of iOS security protocols. As technology advances, maintaining and updating such tools becomes crucial for security researchers and developers alike.
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Google ClusterFuzz
ClusterFuzz is a comprehensive fuzzing framework aimed at identifying security weaknesses and stability issues within software applications. Used extensively by Google, it serves as the testing backbone for all its products and functions as the fuzzing engine for OSS-Fuzz. This powerful infrastructure comes equipped with numerous features that enable the seamless integration of fuzzing into the software development process. It offers fully automated procedures for filing bugs, triaging them, and resolving issues across various issue tracking platforms. Supporting multiple coverage-guided fuzzing engines, it enhances outcomes through ensemble fuzzing and a range of fuzzing techniques. Moreover, the system provides statistical data to evaluate the effectiveness of fuzzers and track the frequency of crashes. Users benefit from a user-friendly web interface that streamlines the management of fuzzing tasks and crash analysis. ClusterFuzz also accommodates various authentication methods via Firebase, and it boasts functionalities for black-box fuzzing, reducing test cases, and pinpointing regressions through bisection. In conclusion, this powerful tool not only elevates software quality and security but also becomes an essential asset for developers aiming to refine their applications, ultimately leading to more robust and reliable software solutions.
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