Posted on 2021-02-26Deandre Hall
The staff at InQuest have been busy running a variety of different research experiments in the realm of bleeding-edge maldoc discovery to ensure the efficacy of detection for our customers and generate threat intelligence. One such experiment is our Twitter bot that tweets about malicious stage-2 RTFs referenced from documents found within the InQuest Labs Corpus. Another additional research project includes the mass curation and password cracking attempts of encrypted files.
Posted on 2021-01-26Josiah Smith
Throughout InQuest's research into detecting maldocs, deserving attention has been given to the graphical asset that is used as the coercive lure. From "Worm Charming", InQuest's Malware Lures Gallery, and Optical Character Recognition inspection of the instructive text to enable embedded logic, uncountable wins have been brought to the community's attention. This quick blog details a couple of approaches for acquiring maldoc images without the need to open the document and copy the image.
Posted on 2021-01-25David Ledbetter
On December 16th, 2020 Twitter user Insomnihack @pro_integritate posted an interesting obfuscated document, where it was flagged as Dridex in some sandboxes. This sample threw an error and would not open in Office 2010 until I changed the file extension to “doc’. The thing that stood out the most on initial inspection is the massive use of the properties “wd.. “ like “wdArtWeavingStrips” each of these properties map to constant values of “Word Enumerated Constants”
Posted on 2020-12-30Josiah Smith
Social engineering is a common, low-tech approach where a threat actor impersonates someone else to obtain sensitive information or persuade the deceived to comply with some other request type. It has been described as “hacker-speak” for tricking a person into disclosing authentication information, executing malicious code, or opening a door. Some classic example of social engineering is the promise of funds from the prince of Nigeria, and the process has matured into malicious documents with coercive DocuSign lures or spoofed invoice scams changing the routing information for payments.
Posted on 2020-12-16Steve Esling
While two different malware samples might appear completely different to a human's evaluation, those same samples, stripped of their identities and reduced down to a vectorized representation of their most important qualities, might be found by a machine to have been twins all along. Insights like this are the goal of "clustering," a machine learning technique based on finding the similarities and differences across and between a massive amount of data points. What follows is an overview of one of those techniques, K-means.