The Fine Print

Specialized Cyber Defense & Operational Agents

Problem Statement

Red Teams conduct adversary emulation to test security controls, but running manual penetration tests is resource-intensive and conducted infrequently. Automated security control validation requires agents capable of dynamically generating safe emulation commands mapped to MITRE ATT&CK techniques.

Goal

Implement an agent that generates safe, localized adversary emulation scripts (e.g., Atomic Red Team tests) for testing specific security controls.

Outcome Measurement

1. Testing Frequency: Continuous automated control testing enabled across infrastructure daily.
2. Coverage Verification: Validated EDR detection capabilities against 100% of targeted MITRE ATT&CK techniques.

How the Code was Built

Synthesizes CLI emulation actions with built-in cleanup routines to ensure host stability.

Executable Code

from pydantic import BaseModel, Field
from langchain_openai import ChatOpenAI
class EmulationTest(BaseModel):
    technique_id: str
    technique_name: str
    emulation_command: str
    cleanup_command: str
    expected_edr_alert: str
def generate_adversary_emulation(mitre_technique_id: str) -> EmulationTest:
    llm = ChatOpenAI(model="gpt-4o", temperature=0)
    structured_llm = llm.with_structured_output(EmulationTest)
    prompt = f"Generate a safe Atomic Red Team adversary emulation command and cleanup command for MITRE Technique '{mitre_technique_id}'."
    return structured_llm.invoke(prompt)
if __name__ == "__main__":
    res = generate_adversary_emulation("T1059.001")
    print(f"Technique: {res.technique_id} - {res.technique_name}")
    print(f"Emulation Execution:\n  {res.emulation_command}")
    print(f"Cleanup Action:\n  {res.cleanup_command}")