June 21, 2021


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Welcome to ThreatPursuit VM: A Threat Intelligence and Hunting Virtual Machine

Welcome to ThreatPursuit VM: A Threat Intelligence and Hunting Virtual Machine

Welcome to ThreatPursuit VM: A Threat Intelligence and Hunting Virtual MachineSkilled adversaries can deceive
detection and often employ new measures in their tradecraft. Keeping a
stringent focus on the lifecycle and evolution of adversaries allows
analysts to devise new detection mechanisms and response processes.
Access to the appropriate tooling and resources is critical to
discover these threats within a timely and accurate manner. Therefore,
we are actively compiling the most essential software packages into a
Windows-based distribution: ThreatPursuit VM.

ThreatPursuit Virtual Machine (VM) is a fully customizable,
open-sourced Windows-based distribution focused on threat intelligence
analysis and hunting designed for intel and malware analysts as well
as threat hunters to get up and running quickly. The threat
intelligence analyst role is a subset and specialized member of the
blue team. Individuals in this role generally have a strong impetus
for knowing the threat environment. Often their traits, skills and
experiences will vary depending on training and subject matter expertise.

Their expertise may not be technical and may include experiences and
tradecraft earned by operating within a different domain (e.g.,
geospatial, criminal, signals intelligence, etc.). A key aspect of the
role may include the requirement to hunt, study and triage previously
undiscovered or recently emerging threats by discerning data for evil.
Threat analysts apply a variety of structured analytical methods in
order to develop meaningful and relevant products for their customers.

With this distribution we aim to enable users to:

  • Conduct hunting activities or missions
  • Create
    adversarial playbooks using evidence-based knowledge
  • Develop and apply a range of analytical products amongst
  • Perform analytical pivoting across forensic
    artifacts and elements
  • Emulate advanced offensive security
  • Enable situational awareness through intelligence
    sharing and reporting
  • Applied data science techniques &
    visualize clusters of symbolic data
  • Leverage open
    intelligence sources to provide unique insights for defense and

Akin to both FLARE-VM
and Commando
, ThreatPursuit VM uses Boxstarter, Chocolatey and MyGet packages to install software
that facilitates the many aspects related to roles performed by
analysts. The tools installed provide easy access to a broad range of
tooling, including, but not limited to, threat analytics, statistics,
visualisation, threat hunting, malware triage, adversarial emulation,
and threat modelling. Here are some of the tools, but there are many more:

For a full list of tools, please visit our GitHub repository.


Similar to FLARE-VM and Commando VM, it’s
recommended to install ThreatPursuit VM in a virtual machine. The
following is an overview of the minimal and recommended installation requirements.

  • Windows 10 1903 or greater
  • 60 GB Hard Drive
  • 4 GB RAM
  • Windows 10 1903
  • 80+ GB Hard Drive
  • 6+ GB
  • 1 network adapter
  • OpenGL Graphics Card
  • Enable Virtualization support for VM
    • Required
      for Docker (MISP, OpenCTI)
Standard Install

The easiest way to install ThreatPursuit VM is to use the following
steps. This will install all the default tools and get you finding
evil in no time!

  1. Create and configure a new Windows 10 VM with the
    aforementioned requirements.

    • Ensure VM is updated
      completely. You may need to check for updates, reboot and check
      again until no more remain.
  2. Install your
    specific VM guest tools (e.g., VMware Tools) to allow additional
    features such as copy/paste and screen resizing.
  3. Take a
    snapshot of your machine! This allows you to always have a clean
  4. Download and copy

    to your newly configured VM.
  5. Open PowerShell as an

Next, unblock the install file by running: Unblock-File
, as seen in Figure 1.

Figure 1: Unblock-File installation script

Enable script execution by running: Set-ExecutionPolicy
Unrestricted -f 
, as seen in Figure 2.

Figure 2: Set-ExecutionPolicy
Unrestricted -f script

Finally, execute the installer script as follows: .install.ps1

After executing install.ps1, you’ll be prompted for the
administrator password in order to automate host restarts during
installation as several reboots occur. Optionally, you may pass your
password as a command-line argument via “.install.ps1
-password <password>”
. If you do not have a password
set, hitting enter when prompted will also work.

This will be the last thing you will need to do before the
installation is unattended. The script will set up the Boxstarter
environment and proceed to download and install the ThreatPursuit VM
environment, as seen in Figure 3.

Figure 3: Installation script execution

The installation process may take upwards of several hours depending
on your internet connection speed and the web servers hosting the
various files. Figure 4 shows the post-installation desktop
environment, featuring the logo and a desktop shortcut. You will know
when the install is finished with the VM’s logo placed on the background. 

Figure 4: ThreatPursuit VM desktop installed

Custom Install

Is the standard installation too much for you? We provide a custom
installation method that allows you to choose which chocolatey
packages get installed. For additional details, see the Custom
steps at our GitHub repository.

Installing Additional Packages

Since ThreatPursuit VM uses the Chocolatey Windows package manager,
it’s easy to install additional packages not included by default. For
example, entering the command cinst github as administrator
installs GitHub Desktop on your system.

To update all currently installed packages to their most recent
versions, run the command cup all as administrator.

Getting Started: A Use Case

As threat analysts, what we choose to pursue will depend on the
priorities and requirements of our current role. Often, they vary with
each threat or adversary encountered such as financial crime,
espionage, issue-motivated groups or individuals. The role broadly
encompasses the collection and analysis of threat data (e.g., malware,
indicators of attack/compromise) with the goal of triaging the data
and developing actionable intelligence. For example, one may want to
produce detection signatures based on malware network communications
to classify, share or disseminate indicators of compromise (IOCs) in
standardized ways. We may also use these IOCs in order to develop and
apply analytical products that establish clusters of analogous nodes
such as MITRE ATT&CK tactics and techniques, or APT groups. On the
other hand, our goal can be as simple as triaging a malware sample
behavior, hunting for indicators, or proving or disproving a
hypothesis. Let’s look at how we might start.

Open Hunting

To start our use case, let’s say we are interested in reviewing
latest threat actor activity reported for the quarter. We sign in to
the Mandiant
 portal (Figure 5) using our public subscription to get a
snapshot view of any highlighted activity (Figure 6).

Figure 5: Mandiant Advantage portal

Figure 6: Actor activity for Q3 2020

Based on Mandiant Advantage report, we notice a number of highly
active APT and FIN actors. We choose to drill in to one of these
actors by hovering our mouse and selecting the actor tag FIN11.

We receive a high-level snapshot summary view of the threat actor,
their targeted industry verticals, associated reports and much more,
as seen in Figure 7. We also may choose to select the most recent
report associated with FIN11 for review.

Figure 7: FIN11 actor summary

By selecting the “View Full Page” button as seen at the top right
corner of Figure 6, we can use the feature to download indicators, as
seen in the top right corner of Figure 8.

Figure 8: Full FIN11 page

Within the FIN11 report, we review the associated threat
intelligence tags that contain finished intelligence products.
However, we are interested in the collection of raw IOCs (Figure 9)
that we could leverage to pivot off or enrich our own datasets.

Figure 9: Downloaded FIN11 indicators

Using the Malware
Information Sharing Platform (MISP
)as our collection point, we
are going to upload and triage our indicators using our local MISP
instance running on ThreatPursuit VM.

Please note you will need to ensure your local MISP instance is
running correctly with the configuration of your choosing. We select
the “Add Event” button, begin populating all needed fields to prepare
our import, and then click “Submit”, as shown in Figure 10.

Figure 10: MISP triage of events

Under the tags section of our newly created FIN11 event, we apply
relevant tags to begin associating aspects of contextual information
related to our target, as seen in Figure 11.

Figure 11: MISP Event setup for FIN11

We then select “Add Attribute” into our event, which will allow us
to import our MD5 hashes into the MISP galaxy, as seen in Figure 12.
Using both the category and type, we select the appropriate values
that best represent our dataset and prepare to submit that data into
our event.

Figure 12: MISP import events into FIN11 event

MISP allows for a streamlined way to drill and tag indicators as
well as enrich and pivot with threat intelligence. We can also choose
to perform this enrichment process within MISP using a variety of open
intelligence sources and their modules, such as Mandiant
, PassiveTotal,
Shodan and VirusTotal. We
can also achieve the same result using similar tools already packaged
in ThreatPursuit VM.

Using Maltego CE, installed as part of the VM, we can automate
aspects of targeted collection and analysis of our FIN11 malware
families and associated infrastructure. The following are just some of
the Maltego plugins that can be configured post installation to help
with the enrichment and collection process:

Targeting the suspected payload, we attempt to pivot using its MD5
hash value (113dd1e3caa47b5a6438069b15127707) to discover additional
artifacts, such as infrastructure, domain record history, previously
triaged reports, similar malware samples, timestamps, and the rich headers.

Importing our hash into Maltego CE, we can proceed to perform a
range of queries to hunt and retrieve interesting information related
to our FIN11 malware, as seen in Figure 13.

Figure 13: Maltego CE querying MD5 hash

Quite quickly we pull back indicators; in this case, generic named
detection signatures from a range of anti-malware vendors. Using
VirusTotalAPI Public, we perform a series of collection and triage
queries across a variety of configured open sources, as shown in
Figure 14.

Figure 14: Automating enrichment and
analysis of targeted infrastructure

A visual
link has been made public
for quick reference.  

With our newly identified information obtained by passively scraping
those IOCs from a variety of data providers, we can identify
additional hashes, delivery URLs and web command and control
locations, as shown in Figure 15.

Figure 15: Maltego visualization of FIN11 dropper

Pivoting on the suspected FIN11 delivery domain near-fast[.]com, we
have found several more samples that were uploaded to an online
malware sandbox website AppAnyRun.
Within the ThreatPursuit VM Google Chrome browser and in the Tools
directory, there are shortcuts and bookmarks to a range of sandboxes
to help with accessing and searching them quickly. We can use AppAnyRun to further analyze the
heterogenous networks and execution behaviors of these acquired samples.

We have identified another similar sample, which is an XLS document
named “MONITIORING REPORT.xls” with the MD5 hash
5d7d2371668ad4a6484f76b0b6511961 (Figure 16). Let’s attempt to triage
this newly discovered sample and qualify the relationship back to FIN11.

Figure 16: VirusTotal execution report of 5d7d2371668ad4a6484f76b0b6511961

Extracting interesting strings and indicators from this sample
allows us to compare these artifacts against our own dynamic analysis.
If we can’t access the original malware sample, but we have other
indicators to hunt with, we could also pivot on various unique
characteristics and attributes (e.g., imphash, vthash, pdb string,
etc…) to discover related samples.

Even without access to the sample, we can also use YARA to mine for
similar malware samples. One such source to mine is using the mquery tool and their datasets offered
via CERT.PL. To fast track the creation of a YARA rule, we leverage
the FIN11 YARA rule provided within the FIN11
Mandiant Advantage report
. Simply copy and paste the YARA rule
into mquery page and select “Query” to perform the search (Figure 17).
It may take some time, so be sure to check back later (here are the results).

Figure 17: mquery YARA rule hunting
search for FIN11 malware

Within our mquery search, we find a generic signature hit on
Win32_Spoonbeard_1_beta for the MD5 hash
3c43d080b5badfdde7aff732c066d1b2. We associate this MD5 hash with
another sandbox, app.any.run, at the following URL:

  • https://app.any.run/tasks/19ac204b-9381-4127-a5ac-d6b68e0ee92c/

As seen in Figure 18, this sample was first uploaded on May 2, 2019,
with an associated infection chain intact.

Figure 18: AppAnyRun Execution Report on 3c43d080b5badfdde7aff732c066d1b2

We now have a confident signature hit, but with different named
detections on the malware family. This is a common challenge for
threat analysts and researchers. However we have gained interesting
information about the malware itself such as its execution behavior,
encryption methods, dropped files, timelines and command and control
server and beacon information. This is more than enough for us to
pivot across our own datasets to hunt for previously seen activities
and prepare to finalize our report.

Once we are confident in our analysis, we can start to model and
attribute the malware characteristics. We can leverage other threat
exchange communities and intelligence sources to further enrich the
information we collected on the sample. Enrichment allows the analysts
to greater extrapolate context such as timings, malware similarity,
associated infrastructures, and prior targeting information. We will
briefly add our content into our MISP instance and apply tags to
finalize our review.

We may wish to add MITRE ATT&CK tags (Figure 19) relevant across
the malware infection chain for our sample as they could be useful
from a modelling standpoint.

Figure 19: MITRE ATT&CK tags for the
malware sample

Final Thoughts

We hope you enjoyed this basic malware triage workflow use-case
using ThreatPursuit VM. There are so many more tools and capabilities
within the included toolset such as Machine learning (ML) and ML
algorithms, that also assist threat hunters by analyzing large volumes
of data quickly. Check out some of FireEye’s ML blog posts here.

For a complete list of tools please see the ThreatPursuit VM
GitHub repository
. We look forward to releasing more blog posts,
content and playbooks as our user base grows.

And finally, here are some related articles that might be of interest.

Malware Analysis

Digital Forensics

Intelligence Analysis and Assessments