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The problem

Traditional honeypots have a fatal flaw: they all look the same. Once an attacker (or AI agent) learns the signature of a particular honeypot, they can avoid every identical deployment worldwide.

The solution

On first run, Sundew generates a deployment persona -a coherent identity that shapes every aspect of the honeypot:
This persona determines:
  • Endpoint paths -/api/v2/transactions vs /v1/patients vs /graphql
  • Response bodies -financial data, healthcare records, SaaS metrics
  • HTTP headers -Server, X-Powered-By, error formats
  • Timing profiles -simulated latency matching real-world services
  • Auth schemes -bearer JWT, API keys, OAuth2, basic auth
  • MCP tool names -query_transactions vs get_patient_records
  • Error formats -RFC 7807, simple JSON, HTML, XML

Generation methods

Personas can be generated three ways:

Supported industries

Anti-detection

Sundew’s test suite (test_anti_detection.py) deploys 5 instances with different personas and verifies zero cross-instance correlation across:
  • Response body structure and field names
  • HTTP header combinations
  • Error message formatting
  • Endpoint path patterns
  • Timing distributions