About this project
The Problem DFIR teams rely on a fragmented toolbox: Volatility for memory forensics, tshark for network capture, exiftool for metadata, objdump for binary analysis, binwalk for carving, steghide for steganography. No two machines carry the same set — jump-boxes, SOC laptops, cloud sandboxes, and honeypot collectors all differ. Existing fixes either hardcode brittle integrations that crash when a tool is missing, or force bloated all-in-one distros just for consistency. Connecting a private, self-hosted LLM to this evidence via MCP today means hand-wiring the integration per machine — if a utility is absent, the server breaks, causing costly delays during time-sensitive incidents. The Solution This framework adds a runtime capability-discovery layer that flips the integration model. At startup, the server inspects the host — probing binaries, environment paths, and container engines, including health checks like verifying an operational Docker daemon instead of a fragile host Python install — to learn exactly what's usable. It then runs three automated steps: **Discovery & Verification** (scan and validate local or containerized tools), **Dynamic Mapping** (map each tool's inputs/outputs to standard MCP definitions), and **Context Assembly** (self-assemble matching Tools, Resources like magic-byte signatures and case logs, and triage Prompts). Missing utilities are skipped gracefully, never crashing the server. Evidence never leaves the local perimeter, making it suited to air-gapped, high-compliance environments. Because it's built on NitroStack's decorator-based SDK, this whole pattern can itself become a reusable template — scaling to new tools or commercial forensic suites needs only a manifest update, not a rebuild.
Open Innovation track
Solve any real-world problem with AI, regardless of industry or domain.