Specialized Cyber Defense & Operational Agents
Problem Statement
Threat actors frequently post compromised corporate credentials, database dumps, and internal access keys on underground dark web forums and paste sites. Security teams cannot manually monitor hundreds of onion sites continuously.
Goal
Build an automated agent that ingests dark web forum scrapings, identifies corporate domain mentions, and alerts security teams to credential exposure.
Outcome Measurement
- Detection Speed: Credential leaks discovered within minutes of public posting.
- Mitigation Rate: Automated credential revocation triggered immediately upon alert validation.
How the Code was Built
Filters dark web text dumps for corporate domain indicators and extracts exposed credential pairs.
Executable Code
from pydantic import BaseModel, Fieldfrom langchain_openai import ChatOpenAIclass DarkWebLeakAlert(BaseModel): domain_matched: str exposed_email: str credential_hash_type: str leak_severity: str immediate_action: strdef parse_dark_web_dump(forum_post_text: str, target_domain: str) -> DarkWebLeakAlert: llm = ChatOpenAI(model="gpt-4o", temperature=0) structured_llm = llm.with_structured_output(DarkWebLeakAlert) prompt = f"Analyze dark web paste dump for leaked credentials belonging to target domain '{target_domain}':\n{forum_post_text}" return structured_llm.invoke(prompt)if __name__ == "__main__": dump_text = "LEAKED DATABASE snippet: admin@company.com:$2a$12$e89abc1234567890 (Hash type: bcrypt) - Selling full dump on forum." res = parse_dark_web_dump(dump_text, "company.com") print(f"Target Email: {res.exposed_email}") print(f"Severity: {res.leak_severity}") print(f"Action: {res.immediate_action}")
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