Tag: Python

Growing Your Malware Corpus

DFIR, Malware, Python, yara

If you’re writing YARA rules or doing other kinds of detection engineering, you’ll want to have a test bed that you can run your rules against.  This is known as a corpus. For your corpus you’ll want to have both Goodware (known good operating system files), as well as a library of malware files.

One source to get a lot of malware samples is from VX-Underground.  What I really appreciate about VX-Underground is that in addition to providing lots of malware samples, they also produce an annual archive of samples and papers. You can download a whole year’s worth of samples and papers, from 2010 to 2023.

Pandora’s Box

Just to understand the structure here, I have a USB device called “Pandora.” On the root of the drive is a folder called “APT”, and within that is a “Samples” directory. Inside the samples directory is the .7z download for 2023 from VX-Underground. There’s also a python script… we’ll get to that soon enough.

The first thing we’ll need to do is unzip the download with the usual password.

7zz x 2023.7z

Once the initial extraction is complete you can delete the original 2023.7z archive.

Within the archive for each year, there is a directory for the sample, with sub-directories of ‘Samples’ and ‘Papers.’  Every one of the samples is also password protected zip file.

This makes sense from a safety perspective, but it makes it impossible to scan against all the files at once.

Python to the Rescue

We can utilize a Python script to recursively go through the contents of our malware folder and unzip all the password protected files, while keeping those files in their original directories.

You may have noticed in the first screenshot that I have a script called ExtractSamples.py in my APT directory.

We will use this for the recursive password protected extractions.

Python ExtractSamples.py

A flurry of code goes by, and you congratulate yourself on you Python prowess. Now if we look again at our contents, we’ve got the extracted sample and the original zip file. 

Let’s get rid of all the zip files as we don’t need them cluttering up the corpus.

We can start by running a find command to identify all the 7zip files.

find . -type f -name '*.7z' -print

After you’ve checked the output and verified the command above is only grabbing the 7z files you want to delete, we can update the command to delete the found files.

find . -type f -name '*.7z' -delete

One more a directory listing to verify:

Success. All the 7z files are removed and all the sample files are intact.

GitHub Link: ExtractSamples.py

Time to go write some new detections!

Huntress CTF: Week 1 – Malware: Hot Off The Press, HumanTwo, PHP Stager & Zerion

CTF, DFIR, Malware, PowerShell

Hot Off The Press

To start with let’s see what kind of file this is.

“UHARC is a compression/archiving system for PC platforms, which appears to be neglected since around 2005. It achieves better compression than most other archivers, at the expense of being much slower.” 

http://fileformats.archiveteam.org/wiki/UHARC

I scoured the internet looking for a copy of UHARC to download. I’m not going to link any here as many if not all may contain malware. Since this is a Windows only tool, (or Wine under Linux), we’ll open this one in a sandboxed Windows system.

When the file extracts we are presented with hot_off_the_press.ps1.

OMG that’s a lot of obfuscation! Let’s see if we can clean this up and make it more readable. First let’s remove all the ”+”

That’s a little bit better. There’s another obfuscation method going on where specific numbers are used to represent different letters. Originally, I tried to determine the substitution by completing terms I knew. Early ahead I saw (”Sc{2}i’pt{1}loc{0}Logging”) which to me reads like ScriptBlockLogging. So all 2’s are i’s, 1’s are B’s, and 0’s are k. I do a find/replace through the script with replacements on {0},{1}, and {2}. Now it looks like a block of Base64 in the middle block. I copy it over to CyberChef and … NADA. Something’s not right.

If you look closer at the code, you’ll see that each one of the strings that had a {#} substitution in it ends with “-f” followed by other letters in quotations. The first character after -f is substituted for {0}, the next for {1}, etc. So I run the same substitution pattern on the script using the correct letters for this string this time.

Replace the {0} with L.

Replace the {1} with E.

Now we’ve got a nice clean block of Base64.

Bring that over to CyberChef for decoding and:

We’ve got a script within the script.

If you scroll down in the output, you’ll see that there’s something else encoded as well.

We’ll run that through CyberChef.

Interesting we have an encoded_flag. Let’s add URL decode to the recipe.

HumanTwo

There were 1,000 files in the zip container. Easy comparison options like file size, modification date etc. don’t help as they are the same for all the files. It’s something in the content that has to be different. How the ‘f’ am I going to find the outlier in 1,000 files?! Meld and diff are two options coming up in the Discord. I install Meld, which is really a gui for diff, and start getting a feel for it. You can compare files or directories. If doing files you could do a 3 way comparison between 3 files. But not 1000. As I was looking through the files with Meld it struck me that all of the file contents we also the same with the exception of one line.

Let’s run through all the files with the_silver_searcher and isolate on String.Equals

Scrolling down through the output we see that one is a definite outlier, or as we like to say around here, an Irregular.

Once more to CyberChef, this time from Hex.

PHP Stager

Heavily obfuscated PHP. This is going to be fun.

Let’s see if ChatGPT can give some insight into what’s going on here.

After several hours of back and forth from PHP to Python to PowerShell, online IDE’s, more ChatGPT, googling, and back again I was able to roughly reproduce the PHP in a Python and get it to execute.

Looks like we’re not done yet. In the middle of the output we can see another block of Base64. What happens if we toss that into CyberChef.

Great! Now we have a Perl script. How far down does this challenge go? It’s like those Matryoshka dolls from Russia. One inside another inside another. But wait… there’s something interesting in the Perl script.

There’s a reference to UU encoding and a string. We’ll copy the string and bring it over to another of my favorite decoding sites, dcode.fr.

Sure enough it handles the decoding and we have our flag.

Zerion

Yay (said no one), another crazy PHP file.

Looks to be using Base64 encoding, Rot13, and some other options to obfuscate the code. Back to school (ChatGPT) to see what’s going on.

Let’s copy the large encoded text block to CyberChef. We’ll apply Rot13, then Reverse the text by Character, and finally – decrypt using Base64.

And that’s our flag!

Use the tag #HuntressCTF on BakerStreetForensics.com to see all related posts and solutions for the 2023 Huntress CTF.

Creating YARA files with Python

DFIR, Malware, Python, yara

When I’m researching a piece of malware, I’ll have a notepad open (usually VS Code), where I’m capturing strings that might be useful for a detection rule. When I have a good set of indicators, the next step is to turn them into a YARA rule.

It’s easy enough to create a YARA file by hand. My objective was to streamline the boring stuff like formatting and generating a string identifier ($s1 = “stringOne”) for each string. Normally PowerShell is my goto, but this week I’m branching out and wanted to work on my Python coding.

The code relies on you having a file called strings.txt. One string per line.

When you run the script it will prompt for (metadata):

  • rule name
  • author
  • description
  • hash

It then takes the contents of strings.txt and combines those with the metadata to produce a cleanly formatted YARA rule.

Caveats:

If the strings have special characters that need to be escaped, you may need to tweak the strings in the rule after it’s created.

The script will define the condition “any of them”. If you prefer to have all strings required, you can change line 22 from

yara_rule += '\t\tany of them\n}\n'

to

yara_rule += '\t\tall of them\n}\n'

CreateYARA.py

def get_user_input():
    rule_name = input("Enter the rule name: ")
    author = input("Enter the author: ")
    description = input("Enter the description: ")
    hash_value = input("Enter the hash value: ")
    return rule_name, author, description, hash_value

def create_yara_rule(rule_name, author, description, hash_value, strings_file):
    yara_rule = f'''rule {rule_name} {{
    meta:
    \tauthor = "{author}"
    \tdescription = "{description}"
    \thash = "{hash_value}"

    strings:
    '''
    with open(strings_file, 'r') as file:
        for id, line in enumerate(file, start=1):
            yara_rule += f'\t$s{id} = "{line.strip()}"\n\t'
    yara_rule += '\n'
    yara_rule += '\tcondition:\n'
    yara_rule += '\t\tany of them\n}\n'

    return yara_rule

def main():
    rule_name, author, description, hash_value = get_user_input()
    strings_file = 'strings.txt'  

    yara_rule = create_yara_rule(rule_name, author, description, hash_value, strings_file)
    print("Generated YARA rule:")
    print(yara_rule)
    
    yar_filename = f'{rule_name}.yar'
    with open(yar_filename, 'w') as yar_file:
        yar_file.write(yara_rule)

    print(f"YARA rule saved to {yar_filename}")

if __name__ == "__main__":
    main()
Sample strings.txt file used as input for the YARA rule
Running CreateYARA.py
YARA rule created from Python script, viewed in VS Code.

Raspberry Pi Internet Speed Monitor

DIY, Python, Raspberry Pi, Steampunk

I was looking wistfully at the Lack Rack from my arm chair, admiring the (faux) copper conduit that covered the primary inbound internet link to the switch. I thought it would be cool looking to have an antique steam gauge attached to the piping. Two things caused that idea to quickly change – 1. the going prices for antique steam gauges right now, 2. once I was thinking about it as a gauge I thought an ‘internet speed gauge’ would be perfect. Alas, even if said gauge could be acquired without breaking the bank, converting MBPS to PSI and making it functional is above my level of engineering. So on to the next best thing – a Raspberry Pi hack.

Materials:

  • Raspberry Pi (3 or 4) with Raspbian 32-bit OS
  • Case with 3.5 in LCD Display
  • Copper spray paint 😉
  • Attention to detail at the command line

Speedtest CLI

Once you’ve got your Raspberry Pi up and running start with the Installing the Speedtest CLI instructions at https://pimylifeup.com/raspberry-pi-internet-speed-monitor/. Complete steps 1-6. When the article gets to Writing our Speed Test Python Script, you can skip that section. I do recommend it from a learning perspective, but the code from that step won’t be used in the final project.

Assuming this is a new installation, you will need to install InfluxDB and Grafana. Complete the respective instructions for each.

Continue with the primary article’s instructions for Using Grafana to Display your Speedtest Data.

If you’ve made it along this far, you should have a working Grafana dashboard displaying Upload Speed, Download Speed, and Ping (Latency). If you’re hitting a glitch – go back through what you’ve coded and double check that any references to the user (default = Pi) are accurate for the user on your device. You should be seeing updated data based on the frequency you specified in crontab -e.

Install Grafana Kiosk

Next, we want to set up our device as a kiosk, and have it boot and display the Network Speed dashboard automatically.

Install Grafana Kiosk from https://github.com/grafana/grafana-kiosk. For my installation I used the ARM v6 grafana-kiosk.linux.armv6 release.

Running the Dashboard on startup:

We’re going to use a yaml file to store our dashboard configuration:

Create a new file, config.yaml and populate it as such:

general:
  kiosk-mode: full
  autofit: true
  lxde: true
  lxde-home: /home/(user)
target:
  login-method: local
  username: admin
  password: (password)
  playlist: false
  URL: http://localhost:3000/d/bdf20d32-c4ff-4578-a3f4-7a38e1f722b9/network-speed?orgId=1
  ignore-certificate-errors: false

Be sure to substitute the proper ID wherever you see (user). The URL for the dashboard can be copied from the web interface of the dashboard.

Edit /home/(user)/.config/lxsession/LXDE-pi/autostart

Add a line: (one line, may show as wrapped)

@/usr/bin/grafana-kiosk -lxde-home /home/(user) -c /home/(user)/config.yaml

Save & Exit.

Now when you reboot the Pi, the dashboard should come up full screen after login.