Research Findings
Ongoing Study: Autonomous AI Agent Behavior in the Wild
We are currently running a fleet of Sundew honeypot instances in the cloud, each configured with a unique deployment persona to maximize coverage and prevent cross-instance fingerprinting. We’re intentionally keeping deployment details vague for obvious reasons. Every instance mimics a different type of production service. The persona engine ensures no two deployments share response structures, endpoint naming, timing characteristics, or error formats.What We’re Measuring
- Discovery patterns - How do AI agents find and identify target services?
- Reconnaissance behavior - What do agents do in the first 30 seconds after connecting?
- Exploitation sequences - What attack chains do autonomous agents attempt?
- Tool use patterns - Which MCP tools do agents invoke, and in what order?
- Evasion techniques - Do agents attempt to detect or avoid honeypots?
- Cross-instance correlation - Can agents recognize two Sundew instances as the same software?
Data Collection Pipeline
All instances stream structured telemetry to a centralized analysis cluster. Every HTTP request, MCP tool invocation, and credential access attempt is logged with full request/response bodies, timing data, and behavioral metadata. The raw dataset will be published alongside the findings for reproducibility.Status
Data collection is actively underway. We are allowing sufficient time to gather a statistically meaningful sample across all persona types and trap configurations before publishing results.