There are a myriad of post exploitation frameworks that can be deployed and utilized by anyone. These frameworks are great to stand up as a defender to get an insight into what C&C (command and control) traffic can look like, and how to differentiate it from normal user behavior. The following blog post demonstrates an endpoint becoming infected, and the subsequent analysis in RSA NetWitness of the traffic from PowerShell Empire.
The attacker sets up a malicious page which contains their payload. The attacker can then use a phishing email to lure the victim into visiting the page. Upon the user opening the page, a PowerShell command is executed that infects the endpoint and is invisible to the end user:
The endpoint then starts communicating back to the attacker's C2. From here, the attacker can execute commands such as tasklist, whoami, and other tools:
From here onward, the command and control would continue to beacon at a designated interval to check back for commands. This is typically what the analyst will need to look for to determine which of their endpoints are infected.
The Detection Using RSA NetWitness Network/Packet Data
The activity observed was only possible due to the communication happening over HTTP. If this had been SSL, the detection via packets would be much more difficult. This is why introducing SSL Decryption/Interception/Offloading is highly recommended. SSL inspection devices are nothing more than a well-designed man-in-the-middle attack that breaks the encryption into two separate encrypted streams. Therefore, they still provide an adequate level of protection to end-users while allowing security analysts and devices to properly monitor and alert when malicious or unwanted activity takes place, such as the web shells shown here. In summary, if you are responsible for protecting your organization’s assets, you should definitely consider the pros and cons of using this technology.
The analyst begins their investigation by placing a focus on looking for C2 traffic over HTTP. The analyst can then look into pulling apart the characteristics of the protocol by using the Service Analysis meta key. From here they notice a couple interesting meta values to pivot on, http with binary and http post no get no referer directtoip:
Upon reducing the number of sessions to a more manageable number, the analyst can then look into other meta keys to see if there are any interesting artifacts. The analyst look under the Filename, Directory, Client Application, and Server Application meta keys, and observes the communication is always towards a microsft-iis/7.5 server, from the same user agent, and toward a subset of PHP files:
The analyst decides to use this is as a pivot point, and removes some of the other more refined queries, to focus on all communication toward those PHP files, from that user agent, and toward that IIS server version. The analyst now observes additional communication:
Opening up the visualization, the analyst can view the cadence of the communication and observes there to be a beacon type pattern:
Pivoting into the Event Analysis view, the analyst can look into a few more details to see if there suspicions on this being malicious are true. The analyst observes a low variance in payload, and a connection which is taking place ~every 4 minutes:
The analyst reconstructs some of the sessions to see the type of data being transferred, the analyst observes a variety of suspicious GET and POST's with varying data being transferred:
The analyst confirms this traffic is highly suspicious based of the analysis they have performed, the analyst subsequently decides to track the activity with an application rule. To do this, the analyst looks through the metadata associated with this traffic, and finds a unique combination of metadata that identifies this type of traffic:
IMPORTANT NOTE: Application rules are very useful for tracking activity. They are however, very environment specific, therefore an application rule used in one environment, may be of high fidelity, but when used in another, could be incredibly noisy. Care should be taken when creating or using application rules to make sure they work well within your environment.
The Detection Using RSA NetWitness Endpoint Tracking Data
The analyst, as they should on a daily basis, is perusing the IOC, BOC, and EOC meta keys for suspicious activity. Upon doing so, they observe the metadata, browser runs powershell and begin to investigate:
Pivoting into the Event Analysis view, the analyst can see that Internet Explorer spawned PowerShell, and subsequently the PowerShell that was executed:
The analyst decides to decode the base64 to get a better idea as to what the PowerShell is executing. The analyst observes the PowerShell is setting up a web request, and can see the parameters it would be supplying for said request. From here, the analyst could leverage this information and start looking for indicators of this in their packet data (this demonstrates the power behind having both Endpoint, and Packet solutions):
Pivoting in on the PowerShell that was launched, it is also possible to see the whoami and tasklist that was executed as well. This would help the analyst to paint a picture as to what the attacker was doing:
The traffic outlined in this blog post is of a default configuration for PowerShell Empire; it is therefore possible for the indicators to be different depending upon who sets up the instance of PowerShell Empire. With that being said, C2's still need to check-in, C2's will still need to deploy their payload, and C2's will still perform suspicious tasks on the endpoint. The analyst only needs to pick up on one of these activities to start pulling on a thread and unwinding the attackers activity,
It is also important to note that PowerShell Empire network traffic is cumbersome to decrypt. It is therefore important to have an endpoint solution, such as NetWitness Endpoint, that tracks the activities performed on the endpoint for you.
Rui Ataide has been working on a script to scrape Censys.io data looking for instances of PowerShell Empire. The attached Python script queries the Censys.io API looking for specific body request hashes, then subsequently gathers information surrounding the C2, including:
Hosting Server Information
The PS1 Script
Also attached is a sample output from this script with the PowerShell Empire metadata that has currently been collected.