A blog post discussing INF-SCT fetch and execute techniques for bypass, evasion, and persistence
KLara project is aimed at helping Threat Intelligence researchers hunt for new malware using Yara. In order to hunt efficiently for malware, one needs a large collection of samples to search over. Researchers usually need to fire a Yara rule over a collection / set of malicious files and then get the results back. In some cases, the rule needs adjusting. Unfortunately, scanning a large collection of files takes time. Instead, if a custom architecture is used, scanning 10TB of files can take around 30 minutes. KLara, a distributed system written in Python, allows researchers to scan one or more Yara rules over collections with samples, getting notifications by e-mail as well as the web interface when scan results are ready. Features Modern web interface, allowing researchers to 'fire and forget' their rules, getting back results by e-mail / API Powerful API, allowing for automatic Yara jobs submissions, checking their status and getting back results. API Documentation will be released soon. Distributed system, running on commodity hardware, easy to deploy and implement. Architecture KLara leverages Yara's power, distributing scans using a dispatcher-worker mode.
A blog post discussing INF-SCT fetch and execute techniques for bypass, evasion, and persistence
A library for running basic functions from stripped binaries cross platform.
A powerful tool for detecting and identifying malware using a rule-based system.
Abusing the COM Registry Structure: CLSID, LocalServer32, & InprocServer32
A malware processing and analytics tool that utilizes Pig, Django, and Elasticsearch to analyze and visualize malware data.
Scans running processes for potentially malicious implants and dumps them.
