Introduction
Traditional C2 frameworks often present operators with streams of raw command output spread across multiple implants, making it difficult to retain context, revisit earlier findings, or quickly understand what happened during a long-running engagement. This presentation explores how a C2 teamserver can move beyond unstructured logs by parsing operational data into a searchable, machine-readable model.
Execution
Through a demonstration of the new C2 server, the session shows how information gathered from implant check-ins, process listings, Active Directory queries, screenshots, and other operator activity can be stored, referenced, enriched, and synchronized with tools such as BloodHound. It also examines how a documented REST API, public event API, and extensible applications can support custom commands, event-driven workflows, reporting integrations, and automated responses to new observations.
The presentation then demonstrates how this structured context can be provided to an AI agent through the Model Context Protocol. Rather than searching raw logs, the agent can query relevant engagement data, interpret findings, identify opportunities, suggest next steps, configure automations, and perform approved actions. The session also covers model deployment options and configurable guardrails for read access, write access, and implant command execution.
Ultimately, the presentation illustrates how structured C2 data can improve operator awareness, preserve historical context, enable automation, and make AI-assisted workflows more precise and useful during an engagement.
