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scambuster — Defensive engagement & threat intelligence research laboratory. Converts inbound scam emails into actionable IOCs through controlled, policy-driven AI engagement. Multi-agent LLM architecture with adaptive strategy selection. | Kitploit
도구/GitHubGitHub/laugiov/scambuster
Defensive ToolsIndicator of Compromise (IOC) ManagementInformation GatheringPhishingSocial EngineeringThreat IntelligenceEmail SecurityAI Security
GitHublaugiov/scambuster

scambuster

Defensive engagement & threat intelligence research laboratory. Converts inbound scam emails into actionable IOCs through controlled, policy-driven AI engagement. Multi-agent LLM architecture with adaptive strategy selection.

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ScamBuster

Automated Scambaiting Honeypot & Threat Intelligence Platform

CI codecov License: MIT Live Demo

ScamBuster Operations Dashboard

Most security programs block and forget: the mail goes, the attacker's infrastructure and money rails stay unobserved. ScamBuster engages instead, inbound-only and under policy, turning the exchange into IOCs, actor profiles and tagged tactics (why).

ScamBuster was presented at Black Hat USA 2026 in the Human Factor track.

Try it

Hosted demo -- demo.scambuster.ai, login [email protected] / Un1que$trongPassword2024.

Local demo -- make demo-up, then http://localhost:3002: no API key, no account, no mailbox (guide).

How it works

Persona choice is adaptive: epsilon-greedy with UCB1 learns which persona yields most per scam type.

Multilingual by design: detection rules and persona prompts carry non-English content on purpose, so it answers scammers in their own language (details).

What it produces

  • STIX 2.1 bundle per conversation -- indicators, threat-actor, sightings, observed-data, attack-patterns, relationships
  • TAXII 2.1 server with delta sync
  • MISP Event JSON export
  • SIEM export in CEF, ECS or JSON, file or syslog

Standards, not per-vendor connectors. Verified end to end against OpenCTI (what lands where); the other three follow the same standards, untested live.

Each conversation produces a threat-actor:

root@kitploit:~
{
  "type": "threat-actor",
  "name": "ScamBuster Actor - INVESTMENT #02114290",
  "sophistication": "minimal",
  "goals": ["financial-theft"],
  "primary_motivation": "personal-gain",
  "threat_actor_types": ["criminal"],
  "description": "Criminal actor operating investment scam."
}

Actors carry ATT&CK mapping, indicates relationships to every IOC, and a psychological profile (profiling, TTPs, API).

Quick start

root@kitploit:~
git clone https://github.com/laugiov/scambuster.git
cd scambuster
cp .env.dist .env    # edit it first, see below
make quickstart      # build, start, migrate, seed, JWT keys, n8n

Fill these 4 in .env before real use:

Left as placeholders, it boots in demo mode and says so. Quickstart · demo · AI install · production · architecture · roadmap · all docs.

Limitations

  • Email only, inbound only. No SMS, chat or voice; it never writes first.
  • Not a control. It blocks and filters nothing; it does not replace mail security.
  • One verified export path. OpenCTI; TAXII, MISP and SIEM are untested live.
  • No published metric for the TTP module -- it postdates the evaluation window; precision awaits an operator-run audit.
  • Read-only review. The TTP queue is triage only; campaign attribution stays manual.
  • Output depends on the model. Mock replies are synthetic; personas and rules are seed data you tune; demo data is seeded, not live output.
  • One host, your risk. Docker Compose on one machine, no HA; local legality is yours to establish.

Security & ethics

ScamBuster is a defensive research tool, not an offensive weapon.

  • Inbound-only: engages only after the scammer makes contact
  • No unauthorized access: never accesses attacker systems
  • Content filtering: PolicyGuard blocks threats, illegal content, real PII
  • Rate limiting: hard limits on conversations, messages and LLM calls
  • Kill switch: halt at workflow, API, database or infrastructure level
  • GDPR: data minimization, retention policies, encryption at rest

Responsible use is the operator's responsibility. Confirm your deployment is legal where you operate, keep it inbound-only, and never use it to initiate contact, target individuals, harass, or dox. Read the Disclaimer & Responsible Use before deploying.

More: Security & Guardrails, SECURITY.md.

License

Code MIT; docs and dataset CC BY-NC-SA 4.0.

Contributing

See CONTRIBUTING.md, Discussions and Issues.

Contact

Laurent Giovannoni -- scambuster.ai · LinkedIn · SECURITY.md.

도구 다운로드
AgentRole
ScamClassifierCategorizes the scam (13 types), detects language
IocExtractorExtracts 36 IOC types with context
GeneratorWrites the persona-driven reply
ValidatorSafety and quality gate (PolicyGuard + LLM)
ConversationDirectorReads the thread, steers each turn
OrchestratorRuns the pipeline, tracks cost and traces
InjectionDetectorTwo-layer prompt injection analysis
TtpExtractorTags scammer tactics, closed taxonomy
VariableWhat to do
LLM_API_KEYOpenAI key (or LLM_PROVIDER=mock, no key)
HONEYPOT_IMAP_USERHoneypot mailbox (IMAP, receives scams)
HONEYPOT_IMAP_PASSWORDApp password, not the account password
MAILER_DSNSMTP for replies, @ written as %40