Skip to main content

InQuest Blog

Posted on 2021-01-26 by Josiah 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 2021-01-25 by David 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 2020-12-30 by Josiah 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 2020-12-16 by Steve 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.
Posted on 2020-11-24 by Isabelle Quinn
To validate an e-mail security stack's capability in blocking current real-world threats harvested from the wild, InQuest gathers unique malware daily and validates the common cloud e-mail providers (GSuite, O365). Collectively (stacked on top of one another), the providers' default security stacks are capable of detecting between 85% and 95% of these novel attacks. The samples capable of bypassing these stacks are candidates for the InQuest Email Security Assessment.

Blog Archive