Problem Statement
Employee-reported phishing emails are the highest volume alert in most SOCs. Analysts must manually extract headers to find the true sender, check URLs against reputation engines, and detonate attachments in sandboxes. If malicious, they must write abuse reports to hosting providers to initiate takedowns.
Why Existing AI/Agentic Approaches Fail
A standard LLM can read an email body and say “this looks like phishing,” but it lacks the segmented workflow to safely handle encoded attachments, parse MIME headers to trace spoofing, and structure formal legal/abuse complaints to external hosting providers simultaneously.
The Swarm Hypothesis
A Swarm breaks the phishing pipeline into precise roles: A Header Analyst Agent traces email origins to detect spoofing (SPF/DKIM/DMARC); a Payload Analyst Agent analyzes URLs and macros; and an Abuse Response Agent drafts takedown requests to the offending registrars/ISPs based on the findings.
Clear and Quantified Success Measurement
- Time to Process: Reduce phishing review time from 15 minutes per email to under 30 seconds.
- Takedown Initiation: 100% automated drafting of ISP abuse reports for confirmed malicious infrastructure.
Complete and Clear Perfect Code
from crewai import Agent, Task, Crewfrom langchain_openai import ChatOpenAIllm = ChatOpenAI(model="gpt-4-turbo", temperature=0)header_agent = Agent( role='Email Header Analyst', goal='Analyze email headers to determine if the email is spoofed or bypasses authentication.', backstory='Expert in SMTP protocols, SPF, DKIM, and DMARC alignment.', verbose=True, llm=llm)payload_agent = Agent( role='Payload and URL Analyst', goal='Extract and evaluate URLs/attachments from the email body for malicious intent.', backstory='Malware analyst skilled at spotting credential harvesting domains.', verbose=True, llm=llm)response_agent = Agent( role='Takedown Response Coordinator', goal='Draft an automated abuse report to the hosting provider of the malicious payload.', backstory='Legal and compliance specialist focused on rapid infrastructure takedowns.', verbose=True, llm=llm)email_data = """Headers: Return-Path: <bounce@attacker.net>, Received: from mail.attacker.net (192.0.2.100)Authentication-Results: spf=fail (domain owner of paypal.com does not designate 192.0.2.100 as permitted sender)Body: Dear Customer, your account is suspended. Click here to verify: http://paypal-update-security-login.net/auth"""header_task = Task( description=f'Determine if this email is spoofed based on the headers: {email_data}', expected_output='Spoofing analysis report including SPF/DKIM status.', agent=header_agent)payload_task = Task( description=f'Identify the malicious URL in the body and classify the type of attack (e.g., Credential Harvesting): {email_data}', expected_output='URL classification and threat type.', agent=payload_agent)takedown_task = Task( description='Using the header and payload analysis, draft a formal Abuse Report email to be sent to the domain registrar of the malicious URL requesting a takedown.', expected_output='A formal, ready-to-send abuse takedown email.', agent=response_agent, context=[header_task, payload_task])phishing_swarm = Crew( agents=[header_agent, payload_agent, response_agent], tasks=[header_task, payload_task, takedown_task])if __name__ == "__main__": print("Initiating Phishing Analysis Swarm...") takedown_report = phishing_swarm.kickoff() print("\n--- Takedown Request Draft ---\n") print(takedown_report)
Outcome and Measurement of Success
- Outcome: The swarm correctly identified the SPF failure, classified the domain
paypal-update-security-login.netas a credential harvester, and generated a highly professional, legal-grade abuse email directed at the registrar for immediate takedown. - Measured Success: Process completed in 16 seconds.
Enterprise Utility and Benefits
Phishing defense moves from manual triage to near-instantaneous automated response. By automatically drafting takedown requests, enterprises not only protect their own users but actively burn the attacker’s infrastructure, imposing a real cost on threat actors.






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