Cobalt Strike is a premium offensive security tool leveraged by penetration testers and red team members as a way to emulate adversary behavior. The goal is to validate security detection capabilities and processes replicating a real-world intrusion. While Cobalt Strike is a legitimate tool, it is often abused by actual threat actors as a way to gain and maintain persistence into targeted networks.
To manage command and control, Cobalt Strike leverages an implant that uses beacon configuration known as a Malleable Command and Control (Malleable C2) profile. A Malleable C2 profile contains a tremendous number of options to configure the beacon’s functionality, please see Cobalt Strike’s official documentation for specifics on configuring Malleable C2 beacons.
This blog will focus on using the Elastic Stack to collect Cobalt Strike beacon payloads, extract and parse the beacon configurations, and an analysis of the metadata within the configurations. This will all be taken from the memory of targeted Windows endpoints that we’ve collected from our telemetry.
Fleet is an app in Kibana that provides a central place to configure and monitor your Elastic Agents. Fleet uses integrations, which are unified plugins that allow data to be collected from apps and services, and then stored in Elasticsearch. Integrations are added to policies, and Elastic Agents are added to policies.
First, we need to configure the collection of shellcode and malicious memory regions in a Fleet policy. This will collect 4MB of data from memory surrounding shellcode and malicious memory events. It should be noted that this collection may significantly increase the amount of data stored in Elasticsearch.
You can add this to an existing policy or create a new policy. To create a new policy, in Kibana, navigate to Fleet → Agent Policies → Create agent policy. Give your policy a name and description. Optionally, you can disable “System monitoring” and “Agent monitoring” to reduce the amount of system and agent metadata collected from your endpoints. Click on “Create agent policy”.
Next, click on your new policy and click the “Add integration button.
Finally, we’re going to add the memory and shellcode collection options. Click on the integration name (“Endpoint Security”).
Under “Protections”, leave the different protection types selected, but change the Protection level from “Prevent” to “Detect”. This will allow malware to continue to run to allow for more rich event collection. There are several types of Protections (Malware, Memory, etc.), select “Detect” for each type that has Windows as an available “Operating system”; you can uncheck Mac and Linux Operating Systems. If you are enabling this feature for a production environment, leave the Protection levels as “Prevent”
At the bottom of the integration configuration page, you can toggle “Register as antivirus” so that the Elastic Agent is registered as the Antivirus solution, and disable Windows Defender. Click on “Show advanced settings”.
At the very bottom of the advanced settings page, type “true” for the windows.advanced.memory_protection.shellcode_collect_sample and windows.advanced.memory_protection.memory_scan_collect_sample settings, and then click “Save integration”.
Once you have created this specific Fleet policy, you can apply this policy to an endpoint running the Elastic Agent. For specific instructions on how to deploy the Elastic Agent, refer to the official Elastic documentation.
Now that we’ve made a collection policy and applied it to a Windows machine you can target it with a CobaltStrike campaign. Instead of mimicking what a CobaltStrike beacon could look like in a lab, we’re going to use live CobaltStrike beacon payloads from Elastic’s telemetry.
To find Cobalt Strike beacon payloads, you can use the Discover app in Kibana to return events identified as Cobalt Strike. These events are provided by the Elastic Endpoint Security Agent, which identifies Cobalt Strike beacons and modules with the “Windows.Trojan.CobaltStrike” malware signature. A simple Kibana Query Language (KQL) search is as simple as:
KQL search for Cobalt Strike event.category:(malware or intrusion_detection) and rule.name:(Windows.Trojan.CobaltStrike or Windows.Trojan.Cobaltstrike)
Next, let’s filter on documents that have the process.Ext.memory_region.bytes_compressed field (this is a field populated by the windows.advanced.memory_protection.shellcode_collect_sample and windows.advanced.memory_protection.memory_scan_collect_sample settings we configured in the Fleet policy above). To do that we can simply add a filter for the process.Ext.memory_region.bytes_compressed_present field with a value of true.
Finally, add the process.Ext.memory_region.bytes_compressed field to our view so that we can see the value of the field.
We can see that we have 133 examples with data in the process.Ext.memory_region.bytes_compressed field. This field contains the file extracted from the memory of the infected host and then zlib deflated and Base64 encoded.
Now that we’ve collected the file in the Elastic Stack, let’s turn that raw data into a file that we can analyze.
There is a lot of nuance between operating systems on how to decode Base64 and inflate zlib deflated files. If you’d prefer to use your command line or local tools, feel free to do so. That said, CyberChef is a browser-based data parser that is provided for free by the United Kingdom’s Government Communications Headquarters (GCHQ).
Using the CyberChef web application, add the “From Base64” and “Zlib Inflate” recipes.
Click on the disk icon to download the inflated binary.
Running the file command, we can see that this is a Portable Executable (PE) file that can be analyzed by a malware reverse engineer (RE).
Using the file command to validate the file type $ file beacon.exe beacon.exe: PE32 executable (GUI) Intel 80386 (stripped to external PDB), for MS Windows
While an RE can identify a tremendous amount of information, let’s explore what additional information a non-RE can obtain from this file.
In the next release, we’ll use the beacon that we’ve just collected and extract its configuration. With this information, we’ll be able to identify other important elements such as license identifications, watermarks, and atomic indicators.
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