So you want to add a little spice to your indicators of compromise. After all, an IoC without context or attribution is very much like when you learn what hot is. There are many tools available for us to determine how “hot” an IoC is without burning ourselves. We will be focusing mainly on what we can access publicly and use for free.
In this quick, end of the week post, we wanted to touch on the ubiquitous COVID-19 (aka Corona Virus). Sharing an interesting lure, related malware, and some IOCs for colleagues to dig into while society on a whole is relegated to solitude in our homes. Our posting here is in no way comprehensive. There is a myriad of malware campaigns, disinformation operations, and general scamming revolving around the very concerning topic. Our goal is to further awareness and share some knowledge in the process.
In this post, we provide a detailed analysis of an interesting Excel 4.0 XLM macrosheet maldoc distribution campaign that is tied to a variety of executable payloads, a subject matter we'll be covering in a future blog. As of the time of writing, detection rates for this class of attack are relatively low, and these samples happily bypass the internal GSuite and O365 protection mechanisms.
Since YARA rule creation is a highly valuable skill set we approach the lessons slowly, think of "baby steps" from the movie "What About Bob?" as the approach. In keeping the spirit of the process, we feel that the next natural step to take is to learn about the different components that make up the rules and focus on how they are constructed.
This is the first post in an ongoing series about YARA and its exceptional ability to carve inside of binaries, documents, photos, and other types of files to uncover and match patterns. The additional posts in the series will give anyone who is thinking about gaining YARA skills the ability to start from scratch and get comfortable with the tool's functionality. Each post will advance in skill level and include some of the personal and professional standards we follow to instill good habits early on in the learning process.
ThreatIngestor helps you collect threat intelligence from public feeds, and gives you context on that intelligence so you can research it further, and put it to use protecting yourself or your organization. In this post, we will go through the process of making a twitter bot.
In this article, we dissect a sneaky malicious Microsoft Excel XLM file that we caught in the wild. To do so, we utilize a few open source as well as in-house tools to analyze the Excel document. During our analysis, we point out the limitations of a few popular file carving tools, such as foremost and scalpel, in extracting data from this and related samples.
We believe that any security stack, in essence, follows the Swiss cheese model. With each slice of cheese representing a security product, and each hole representing some bypass or evasion. Following best practices and employing a Defense-in-Depth model results in a stacking of these slices, each additional stack reducing the exposure window and minimizing the overall risk to a computing environment.