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Before yesterdayNettitude Labs

Introducing PoshC2 v8.0

We’re thrilled to announce a new release of PoshC2 packed full of new features, modules, major improvements, and bug fixes. This includes the introduction of a brand-new native Linux implant and the capability to execute Beacon Object Files (BOF) directly from PoshC2!

Download and Documentation

Please use the following links for download and documentation:

RunOF Capability

In this release, we have introduced Joel Snape’s (@jdsnape) excellent method to run Cobalt Strike Beacon Object Files (BOF) in .NET, and its integration in PoshC2. This feature has a blog post unto itself available, but essentially it allows existing BOFs to be run in any C# implant, including PoshC2.

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At a high-level, here is how it works:

  • Receive or open a BOF file to run
  • Load it into memory
  • Resolve any relocations that are present
  • Set memory permissions correctly
  • Locate the entry point for the BOF
  • Execute in a new thread
  • Retrieve any data output by the BOF
  • Clean-up memory artifacts before exiting

Read our recent blog post on this for more detail.

SharpSocks Improvements

SharpSocks provides HTTP tunnelled SOCKS proxying capability to PoshC2 and has been rewritten and modernised to improve stability and usability, in addition to having its integration with PoshC2 improved, so that it can be more clearly and easily configured and used.

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RunPE Integration

Last year, Rob Bone (@m0rv4i) and Ben Turner (@benpturner) released a whitepaper on “Process Hiving” along with a new tool “RunPE”, the source code of which can be found here. We have integrated this technique within this release of PoshC2 for ease of use, and it can be executed as follows:

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By default, new executables can be added to /opt/PoshC2/resources/modules/PEs so that PoshC2 knows where to find them when using the runpe and runpe-debug commands shown above.


We’ve added the dllsearcher command which allows operators to search for specific module names loaded within the implant’s current process, for instance:

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GetDllBaseAddress, FreeMemory & RemoveDllBaseAddress

Three evasion related commands were added which can be used to hide the presence of malicious shellcode in memory. getdllbaseaddress is used to retrieve the implant shellcode’s current base address, for example:

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Looking at our process in Process Hacker, we can correlate this base address memory location:

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By using the freememory command, we can then clear this address’ memory space:

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The removedllbaseaddress command is a combination of getdllbaseaddress and freememory, which can be used to expedite the above process by automatically finding and freeing the relevant implant shellcode’s memory space:

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Get-APICall & DisableEnvironmentExit

In this commit we implemented a means for operators to retrieve the memory location of specific function calls via get-apicall, for instance:

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In addition, we’ve included disableenvironmentexit which patches and prevents calls to Environment.Exit() within the current implant. This can be particularly useful when executing modules containing this call which may inadvertently kill our implant’s process.

C# Ping, IPConfig, and NSLookup Modules

Several new C# modules related to network operations were developed and added to this release, thanks to Leo Stavliotis (@lstavliotis). They can be run using the following new commands:

  • ping <ip/hostname >
  • nslookup <ip/hostname>
  • ipconfig

C# Telnet Client

A simple Telnet client module has been developed by Charley Celice (@kibercthulhu) and embedded in the C# implant handler to provide operators the ability to quickly validate Telnet access where needed. It will simply attempt to connect and run an optional command before exiting:

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We have plans to add additional modules such as this one to cover a wider range of services.

C# Registry Module

Another module by Charley Celice (@kibercthulhu) was added. SharpReg allows for common registry operations in Windows. At this stage it currently consists of simple functionalities to search, query, create/edit, delete and audit registry hives, keys, values and data. It can be executed as shown below:

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We’re adding more features to this module which will include expediating certain registry-based persistence, privilege escalation, UAC bypass techniques, and beyond.


PoshGrep can easily be used to parse task outputs. This can be particularly useful when searching for specific process information obtained from a large number of remote hosts. It can be used by piping your PoshC2 command into poshgrep, for example:

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The output task database retains the full output for tracking.


findfile was added, which can be used to search for specific file names and types. In the example below, we search for any occurrences of the file name “password” within .txt files:

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Bringing PoshC2 to Linux

One of the major new features we have incorporated in this release of PoshC2 is our new Native Linux implant, thanks to the great work of Joel Snape (@jdsnape). While it’s fair to say that we spend most of our time on Windows, we find that having the capability to persist on Linux machines (usually servers) can be key to a successful engagement. We also know that many of the adversaries we simulate have developed tooling specifically for Linux. PoshC2 has always had a Python implant which will run on Linux assuming that Python is installed, but we decided that it was time that we advanced our capabilities to a native binary that is harder to detect and has fewer dependencies.

To that end, Posh v8.0 includes a native Linux implant that can run on any* x86/x64 Linux OS with a kernel >= 2.6 (it should work on earlier versions, but we’ve not tested that far back!). It also works on a few systems that aren’t Linux but have implemented enough of the syscall interface (most importantly ESXi hypervisors).


When payloads are created in PoshC2 you will notice a new “native_linux” payload being written on startup:



This is the stage one payload, and when executed will contact the C2 server and retrieve the second stage. The first stage is a statically linked stripped executable, around 1MB in size. The second stage is a statically linked shared library, that the first stage will load in memory using a custom ELF loader and execute (see below for more detail). The dropper has been designed to be as compatible as possible, and so should just work out of the box regardless of what userspace is present.

The aim of the implant is not to be “super-stealthy”, but to emulate a common Linux userspace Trojan. Therefore, the implant just needs to be executed directly; how you do this will obviously depend on the level of access you have to your target.

Once the second stage has been downloaded and executed the implant operates in much the same way as the existing Python implant, supporting many of the same commands, and they can be listed with the help command:



Most notably, the implant allows you to execute other commands as child processes using /bin/sh, run Python modules (again, assuming a Python interpreter is present on your target), and run the linuxprivchecker script that is present in the Python implant.


To meet our needs, we set the following high-level goals:

  • Follow the existing pattern of a small stage one loader, with a second stage being downloaded from the C2 server.
  • A native executable, with as few dependencies as possible and that would run on as many different distributions as possible.
  • Compatibility with older distributions, particularly those with an older kernel.
  • As little written to disk as possible beyond the initial loader.
  • Run in user-space (i.e., not a kernel implant).

This gives us greater flexibility and stealth, and allows us to operate on machines that maybe don’t have Python installed or where a running Python process would be anomalous.

There are a few choices in language and architecture to build native executables. The “traditional” method is to use C or C++ which compiles to an ELF executable. More modern languages, like Golang, are also an option, and have notably been used by some threat groups to develop native tooling. For this project however we decided to stick with C as it lets us implement small and lean executables.

How it Works

The Linux implant comes in two parts, a dropper and a stage two which is downloaded from the C2.

Compilation of the native images can be a bit time consuming, so we have provided binary images in the PoshC2 distribution (you can see the source code here). This means that when a new implant is generated, PoshC2 needs a way to “inject” its configuration into the binary file. All configuration is contained in the dropper, except for a random key and URI which are patched over placeholder values in the stage two binary and is contained in an additional ELF section at the end of the binary. This is injected by PoshC2 using objcopy when a new implant is generated. You should note that at the moment there is no obfuscation or encryption of the configuration so it will be trivially readable with strings or similar.

When the dropper is launched it parses the configuration and connects to the C2 server to obtain the second stage using the configured hosts and URLs.

Loading the Second Stage

Our main aim with the execution of the second stage was to be able to run it without writing any artifacts to disk, and to have something that was easy to develop and compile. Given the above goals, it also needed to be as portable as possible.

The easiest way to do this would be to create a shared library and use the dlopen() and dlsym() functions to load it and find the address of a function to call. Historically, the dlopen() functions required a file to operate on, but as of kernel version 3.17 it is possible to use memfd_create to get a file descriptor for memory without requiring a writable mount point. However, there are two issues with that approach:

  • The musl standard library we are using (see below) doesn’t support dlopen as it doesn’t make sense in a context where everything is statically linked.
  • Ideally, we’d like to support kernels older than 3.17, as although it was released in 2014, we still come across older ones from time to time.

Given these constraints, we implemented our own shared library loader in the dropper. More details can be found in the project readme, but at a high level it’s this:

  • Parses the stage two ELF header, and allocates memory as appropriate.
  • Copies segments into memory as required.
  • Carries out any relocations required (as specified in the relocations section).
  • Finds the address of our library’s entry function (we define this as loopy() because it, well, loops…).
  • Calls the library function with a pointer to a configuration object and a table of function pointers to common functions the second stage needs.

If you want to understand this process in more detail there is an excellent set of articles by Eli Bendersky that go through the process for load time relocation and position independent code.

In theory, the second stage could be any statically linked library, but we’ve not extensively tested the loader. In the future, we’d like to re-use this loader capability to allow additional modules to be delivered to the implant so you can bring your own tooling as needed (for example, network scanning or proxying).

At this point the second stage is now operating and can communicate with the C2, run commands, etc.


One of the key aims for the Linux implant was to make it operate on as many different distributions/versions as possible without needing to have any prior knowledge of what was running before deployment – something that can be difficult to achieve with a single binary.

Normally Linux binaries are “dynamically linked”, which means that when the program is run the OS runtime-linker (usually something like /lib/ld-linux-x86-64.so.2) finds and loads the shared libraries that are needed.

For example, running ldd /bin/ssh, which shows the linked library dependencies, demonstrates that it depends on a range of different system libraries to do things like cryptographic operations, DNS resolutions, manage threads, etc. This is convenient because your binaries end up being smaller as code is reused, however it also means that your program will not run unless that the specific version of the library you linked against at compile time is present on the target system.

Obviously, we can’t always guarantee what will be present on the systems we are deploying on, so to work around this the implant is “statically linked”. This means that the executable contains its code and all of the libraries that it needs to operate in one file and has no dependencies on anything other than the operating system kernel.

The key component that needs to be linked is the “standard library” which is the set of functions that are used to carry out common tasks like string/memory manipulation, and most importantly interface between your application and the OS kernel using the system call API. The most common standard library is the GNU C library (glibc), and this is what you will usually find on most Linux distributions. However, it is fairly large and can be difficult to successfully statically link. For this reason, we decided to use the musl library, which is designed to be simple, efficient and used to produce statically linked executables (for example as on Alpine Linux).

Because the implant comes in two parts, if there are any common dependencies (e.g., we use libcurl to make HTTPS requests) then they would normally have to be statically linked into each binary. This would obviously be inefficient as the process would end up having two copies of the library in memory, one from the dropper and one from the stage two, and the stage two would be unnecessarily large. Therefore, for the larger libraries like libcurl a set of function pointers are provided from the dropper when it executes the stage two, so it can take advantage of the libraries that were already linked into the dropper.

The implant is built for x86 systems, as this means that it will run on both 32- and 64-bit operating systems. Other architectures (e.g., ARM) may follow.

Child Processes

Our implant would be pretty limited without the ability to execute other commands using the system shell. This is easily carried out using the popen() function call in the standard library which executes the given command and opens a pipe so the command’s output can be read. However, some commands (e.g. ping with default arguments) may not exit, and so our implant would “hang” reading the output forever. To get around this, we have written a custom popen() implementation that allows us to launch our subcommand in a custom process group and set an alarm using SIGALRM to kill it after a user-configurable timeout period. Any output written by the process is then read and returned to the C2. This does mean however that long running commands will be prematurely terminated.


We typically find that Linux environments have a lot less scrutiny applied than their Windows counterparts. Nevertheless, they are often hosting critical services and data and so monitoring for suspicious or unusual behaviour should be considered. Many security vendors are starting to release monitoring agents for Linux, and several open-source tools are available.

A full exploration of security monitoring for Linux is out of scope for this post, but some things that might be seen when using this implant are:

  • Anomalous logins (for example SSH access at unusual times, or from an unusual location).
  • Vulnerability exploitation (for example, alerts in NIDS).
  • wget or curl being used to download files for execution.
  • Program execution from an unusual location (e.g. from a temporary directory or user’s home directory).
  • Changes to user or system cron entries.

The dropper itself has very limited operational security so we expect static detection of the binary by antivirus or NIDS to be relatively straightforward in this publicly released version.

It’s also worth reviewing the PoshC2 indicators of compromise listed at https://labs.nettitude.com/blog/detecting-poshc2-indicators-of-compromise.

Full Changelog

Many other updates and fixes have been added in this version and merged to dev, some of which are briefly summarized below. For updates and tips check out @nettitude_labs, @benpturner, @m0rv4i and @b4ggio-su on Twitter.

  • Miscellaneous fixes and refactoring
  • Fixed MSTHA and RegSvr32 quickstart payloads
  • Several runas and Daisy.dll related fixes
  • Improved PoshC2 reports output and style
  • Enforced the consistent use of UTC throughout
  • FComm related fixes
  • Added Native Linux implant and related functionalities from Joel Snape (@jdsnape)
  • Added Get-APICall & DisableEnvironmentExit in Core
  • Updated to psycopg2-binary so it’s not compiled from source
  • Database related fixes
  • RunPE integration
  • Added GetDllBaseAddress, FreeMemory, and RemoveDllBaseAddress in Core
  • Added C# Ping module from Leo Stavliotis (@lstavliotis)
  • Fixed fpc script on PostgreSQL
  • Added PrivescCheck.ps1 module
  • Added C# IPConfig module from Leo Stavliotis (@lstavliotis)
  • Updated several external modules, including Seatbelt, StandIn, Mimikatz
  • Added EventLogSearcher & Ldap-Searcher
  • Added C# NSLookup module from Leo Stavliotis (@lstavliotis)
  • Added getprocess in Core
  • Added findfile, getinstallerinfo, regread, lsreg, and curl in Core
  • Added GetGPPPassword & GetGPPGroups modules
  • Added Get-IdleTime to Core
  • Added PoshGrep option for commands
  • Added SharpChromium
  • Added DllSearcher to Core
  • Updated Dynamic-Code for PBind
  • Added RunOF capability into Posh along with several compiled situational awareness OFs
  • Updated Daisy Comms
  • Added C# SQLQuery module from Leo Stavliotis (@lstavliotis)
  • Added ATPMiniDump
  • Added rmdir, mkdir, zip, unzip & ntdsutil to Core
  • Fix failover retries for C# & Updated SharpDPAPI
  • Updated domain check case sensitivity in dropper
  • Fixed dropper rotation break
  • Added WMIExec and SMBExec modules
  • Added dcsync alias for Mimikatz
  • Added AES256 hash for uploaded files
  • Added RegSave module
  • SharpShadowCopy integration
  • Fixed and updated cookie decrypter script
  • Updated OPSEC Upload
  • Added FileGrep module
  • Added NetShareEnum to Core
  • Added StickyNotesExtract
  • Added SharpShares module
  • Added SharpPrintNightmare module
  • Added in memory SharpHound option
  • Updated Tasks.py to save Seatbelt output
  • Added kill-remote-process to Core
  • Fixed jxa_handler not being imported
  • Updated posh-update script to accept -x to skip install
  • Added process name in implant view from Lefteris Panos (@Lefterispan)
  • Added SharpReg module from Charley Celice (@kibercthulhu)
  • Added SharpTelnet module from Charley Celice (@kibercthulhu)
  • kill-process with no arguments now terminates the implant’s current process following a warning prompt
  • Added hide-dead-implants command
  • Added ability to modify user agent when creating new payloads from Kirk Hayes (@l0gan54k)
  • Added get-acl command in Core

Download now

github GitHub: https://github.com/nettitude/PoshC2

The post Introducing PoshC2 v8.0 appeared first on Nettitude Labs.

Introducing Process Hiving & RunPE

2 September 2021 at 09:00
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Download our whitepaper and tool

This blog is a condensed version of a whitepaper we’ve released, called “Process Hiving”.  It comes with a new tool too, “RunPE”.  You can download these at the links below.


Our process hiving whitepaper can be downloaded here.


RunPE, our accompanying tool, can be downloaded from GitHub.

High quality red team operations are research-led. Being able to simulate current and emerging threats at an accurate level is of paramount importance if the engagement is going to provide value to clients.

One common use case for offensive operations is the requirement to run native executable files or compiled code on the target and in memory. Loading and running these files in memory is not a new technique, but running executables as secondary modules within a Command & Control (C2) framework is rarer, particularly those that support arguments from the host process.

This blog introduces innovative techniques and is a must have tool for the red team arsenal. RunPE is a .NET assembly that uses a technique called Process Hiving to manually load an unmanaged executable into memory along with all its dependencies, run that executable with arguments passed at runtime, including capturing any output, before cleaning up and restoring memory to hide any trace that it was run.

What is it?

The aim of this project is to develop a .NET assembly that provides a mechanism for running arbitrary unmanaged executables in memory. It should allow arguments to be provided, load any libraries that are required by the code, obtain any STDOUT and STDERR from the process execution, and not terminate the host process once the execution of the loaded PE finishes.

This .NET assembly must be able to be run in the normal way in C2 frameworks, such as by execute-assembly in Cobalt Strike or run-exe in PoshC2, in order to extend the functionality of those frameworks.

Finally, as this is to all take place in an implant process, any artefacts in memory should then be cleaned up by zeroing out the memory and removing them or restoring original values in order to better hide the activity.

We’re calling this technique of running multiple PEs from the within the same process ‘Process Hiving’ and the result of this work is the .NET assembly RunPE. In essence this technique:

  • Receives a file path or base64 blob of a PE to run
  • Manually maps that file into memory without using the Windows Loader in the host process
  • Loads any dependencies required by the target PE
  • Patches memory to provide arguments to the target PE when it is run
  • Patches various API calls to allow the target PE to run correctly
  • Replaces the file descriptors in use to capture output
  • Patches various API calls to prevent the host process from exiting when the PE finishes executing
  • Runs the target PE from within the host process, while maintaining host process functionality
  • Restores memory, unloads dependencies, removes patches and cleans up artefacts in memory after executing

Loading the PE

The starting point for the work was @subtee‘s .NET PE Loader utilised in GhostPack’s SafetyKatz. This .NET PE Loader already mapped a PE into memory manually and invoked the entry point, however a few issues remained preventing its use it in an implant process. SafetyKatz uses a ‘slightly modified’ version of Mimikatz as the target PE, critically to not require arguments or exit the process upon completion.

The first step then was to re-use as much of this work as possible and rewrite it to suit our needs – no need to reinvent the wheel when a lot of great work was already done. The modified loader manually maps the target PE into memory, performs any fixups and then loads any dependency DLLs that are not already loaded. The Import Address Table for the PE is patched with the locations of all the libraries once they are loaded, mimicking the real Windows loader.

Patching Arguments

In a Windows process a pointer to the command line arguments is located in the Process Environment Block (PEB) and can be retrieved directly or, more commonly, using the Windows API call GetCommandLine. Similarly, the current image name is also stored in the PEB. With RunPE, the command line and image name are backed-up for when we reset during the clean-up phase and then replaced with the new values for the target PE.

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Preventing Process Exit

Another issue with running vanilla PEs in this way is that when they finish executing the PE inevitably tries to exit the process, such as by calling TerminateProcess.

Similarly, as the RunPE process is .NET, the CLR also tries to shut down once process termination is initiated, so even if TerminateProcess is prevented CorExitProcess will cause any .NET implant to exit.

To circumvent this a number of these API calls are patched to instead jmp to ExitThread. As the entry point of the target PE is to be run in a new thread this means that once it has finished it will gracefully exit the thread only, leaving the process and CLR instead.

These API calls are patched with bytes that use Return Oriented Programming (ROP) to instead call ExitThread, passing an exit code of 0.

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An example of this patch if the ExitThread function was located at 0x1337133713371337 is below:

0: 48 c7 c1 00 00 00 00 mov rcx, 0x0 // Move 0 into rcx for exit code argument
7: 48 b8 37 13 37 13 37 movabs rax, 0x1337133713371337 // Move address of ExitThread into rax
e: 13 37 13
11: 50 push rax // Push rax onto stack and ret, so this value with be the 'return address'
12: c3 ret

We can see this in x64dbg while RunPE is running, viewing the NtTerminateProcess function and noting it has been patched to exit the thread instead.

Fixing APIs

Several other API calls also required patching with new values in order for PEs to work. One example is GetModuleHandle which, if called with a NULL parameter, returns a handle to the base of the main module. When a PE calls this function it is expecting to receive its base address, however in this scenario the API call will in fact return the host process’ binary’s base address, which could cause the whole process to crash, depending on how that address is then used.

However, GetModuleHandle could also be called with a non-NULL value, in which case the base address of a different module will be returned.

GetModuleHandle is therefore hooked and execution jumps to a newly allocated area of memory that performs some simple logic; returning the base address of the mapped PE if the argument is NULL and rerouting back to the original GetModuleHandle function if not. As the first few bytes of GetModuleHandle get overwritten with a jump to our hook these instructions must be executed in the hook before jumping back to the GetModuleHandle function, return execution to after the hook jump.

As with the previous API patches, these bytes must be dynamically built-in order to provide the runtime addresses of the hook location, the GetModuleHandle function and the base address of the target PE.

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As an additional change the PEB is also updated, replacing the base address with that of the target PE so that if any programs retrieve this address from the PEB directly then they get the expected value.

At this point, the target PE should be in a position to be able to run from within the host process by calling the entry point of the PE directly. However, as the intended use case is to be able to use RunPE to execute PEs in memory from with an implant, it is a requirement to be able to capture output from the program.

Capturing Output

Output is captured from the target process by replacing the handles to STDOUT and STDERR with handles to anonymous pipes using SetStdHandle.

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Just before the target PE entry point is invoked on a new thread, an additional thread is first created that will read from these pipes until they are closed. In this way, the output is captured and can be returned from RunPE. The pipes are closed by RunPE after the target PE has finished executing, ensuring that all output is captured.

Clean Up

As Process Hiving includes running multiple processes from within one, long-running host process it is important that any execution of these ‘sub’ processes includes full and proper clean up. This serves two purposes:

  • To restore any changed state and functionality in order to ensure that the host process can continue to operate normally.
  • To remove any artefacts from memory that may cause an alert or artifact if detected through techniques such as in-memory scanning or aid an investigator in the event of a manual triage.

To achieve this, any code change made by RunPE is stored during execution and restored once execution is complete. This includes API hooks, changed values in memory, file descriptors, loaded modules and of course the mapped PE itself. In the case of any particularly sensitive values, such as the command line arguments and mapped PE, the memory region is first zeroed out before it is freed.

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An example of RunPE running unchanged and up-to-date Mimikatz is below, alongside Procmon process activity events for the process.

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Note that there are no sub-processes created, and Mimikatz runs successfully with the provided arguments.

Running a debug build provides more output and allows us to verify that the artefacts are being removed from memory and hooks removed, etc. We can see below that after the clean-up has occurred the ‘new’ DLLs loaded for Mimikatz have either already been cleaned up by Mimikatz itself (the error code 126) or are freed by RunPE and are now no longer visible in the Modules tab of Process Hacker.

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Similarly, the original code on the hooks such as NtTerminateProcess has been restored, which we can verify using a debugger such as x64dbg as below.

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As during Red Team operations Mimikatz.exe is unlikely to exist in the target environment, RunPE also supports loading of binaries from base64 blobs so that they can be passed with arguments down C2 channels. Long, triple dash switches are used in order to avoid conflicts with any arguments to the target PE.

Z:\Downloads\Whitepaper\Export-e0735b6d-feef-40ce-bcc9-8ce00c5523bc\Process Hiving 64777627280b48d586409f800840b2d6\Untitled 24.png

An example of this from a PoshC2 implant below demonstrates the original use case. The implant host process of netsh.exe loads and invokes the RunPE .NET assembly which in turn loads and runs net.exe in the host process with arguments. In this case net.exe is passed as a base64 blob down C2.

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Known Issues & Further Work

There are a number of known issues and caveats with this work in its current state which are detailed below.

  • RunPE only supports x64 bit native Windows PE files.
  • During testing any modern PE compiled by the testers has worked without issues, however issues remain with a number of older Windows binaries such as ipconfig.exe and icacls.exe. Further research is presently ongoing into what specific characteristics of these files cause issues.
  • If the target PE spawns sub-processes itself then those are not subject to Process Hiving and will be performed in the normal fashion. It is up to the operator to understand what the behaviour of the target PE is any other considerations that should be made.
  • RunPE presently calls the entry point of the target PE on a new thread and waits for that thread to finish, with a timeout. If the timeout is reached or if the target PE manipulates that thread, this is undefined behaviour.
  • PEs compiled without ASLR support do not work currently, such as by mingw.

Additionally, further work can be made on RunPE to improve the stealth of the Process Hiving technique:

  • Dependencies of the target PE can be mapped into memory using the same PE loader as the target PE itself and not using the standard Windows Loader. This would bypass detections on API calls such as LoadLibrary and GetProcAddress as well as any hooks placed in those modules by defensive software.
  • For any native API calls that remain, the use of syscalls directly can be explored to achieve the same ends for the same reasons as described above.


For Blue Team members, the best way to prevent this technique is to prevent the attacker from reaching this stage in the kill chain. Delivery and initial execution for example likely provide more options for detecting an attack than process self-manipulation. However, a number of the actions taken by RunPE can be explored as detections.

  • SetStdHandle is called six times per RunPE call, once to set STDOUT, STDERR and STDIN to handles to anonymous pipes and then again to reset them. A cursory monitor of a number and range of processes on the author’s own machine did not show any invocations of this API call as part of standard use, so this activity could potentially be used to detect RunPE.
  • A number of APIs are hooked or modified and then restored as part of every RunPE run such as GetCommandLine, NtTerminateProcess, CorExitProcess, RtlExitUserProcess, GetModuleHandle and TerminateProcess. Continued modification of these Windows API calls in memory is not likely to be common behaviour and a potential avenue to detection.
  • Similarly, the PEB is also continually modified as the command line string and image name are updated with every invocation of RunPE.
  • While the source code can be obfuscated, any attempt to load the default RunPE assembly into a .NET process provides a strong opportunity for detection.


At its core, Process Hiving is a fairly simple process. A PE is manually mapped into memory using existing techniques and a number of changes are made to API calls and the environment so that when the entry point of that PE is invoked it runs in the expected way.

We hope that this technique and the tool that implements it will allow Red Teams to be able to quickly and easily run native binaries from their implant processes without having to deal with many of the pain points that plague similar techniques that already exist.

The source code for RunPE is available at https://github.com/nettitude/RunPE and any further work on the tool can be found there. Contributions and collaboration are also welcome.

Process Hiving Cover 2

Download our whitepaper and tool

This blog is a condensed version of a whitepaper we’ve released, called “Process Hiving”.  It comes with a new tool too, “RunPE”.  You can download these at the links below.


Our process hiving whitepaper can be downloaded here.


RunPE, our accompanying tool, can be downloaded from GitHub.

The post Introducing Process Hiving & RunPE appeared first on Nettitude Labs.

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