logflip-sift-agent

Autonomous NTFS anti-forensics triage for Protocol SIFT: a read-only MCP server and a self-correcting Claude agent built on the logflip detection engine. The agent finds timestomping on a disk image, reasons about each candidate, and self-corrects when the evidence does not add up - while a signed, never-false-confirm engine makes hallucinated findings structurally impossible.

License: MIT  |  SANS FIND EVIL! hackathon  |  Architecture: Custom MCP Server (#2) + Direct Agent Extension (#1)

What it does

logflip-sift-agent turns the deterministic logflip engine (NTFS $LogFile reverse-replay timestomp detection, corroborated across $LogFile, $UsnJrnl, and $SI-vs-$FN) into a fully autonomous incident-response agent:

• Read-only MCP server (sift-mcp) exposes the engine as typed forensic tools and nothing else. There is no write, delete, or shell tool, so the agent physically cannot modify or spoliate the evidence. The guarantee is architectural, not a prompt the model could ignore.

• Self-correcting agent loop (sift-agent) scans the image, investigates each record that disagrees with the journal, and on a single-source anomaly pivots to an independent corroboration channel before concluding. It accepts the engine's bounded verdict, including an honest "cannot confirm."

• Signed audit trail: every finding's verdict traces to the tool execution that derived it (produced_by_seq); a corroborated anomaly links the independent tools that corroborated it (corroborated_by_seq); and journaled findings carry an HMAC-signed evidence leaf a judge can re-verify offline.

It maps to Rob T. Lee's bar for Protocol SIFT directly: the AI directs verified tools and self-corrects; it does not interpret raw bytes or decide verdicts.

Related MCP server: agentropix-mcp

Architecture at a glance

flowchart TD
    A["NTFS image / streams (READ-ONLY)"] --> B
    subgraph B["sift-mcp  (FastMCP)  -- ARCHITECTURAL BOUNDARY 1"]
      B1["scan_image - detect_record - inspect_mft<br/>inspect_usnjrnl - verify_leaf - verify_db<br/>(no write / delete / shell tool exists)"]
    end
    B --> C
    subgraph C["logflip engine (unmodified)  -- ARCHITECTURAL BOUNDARY 2"]
      C1["reverse_replay - mft_parser - usnjrnl - fingerprint<br/>4-gate never-false-confirm + HMAC-signed leaf"]
    end
    C --> D
    subgraph D["sift-agent  (Claude/OpenAI or deterministic policy)  -- PROMPT BOUNDARY"]
      D1["triage heuristic + self-correction + max-iterations cap"]
    end
    D --> E["verdict guard  -- ARCHITECTURAL BOUNDARY 3<br/>(claim clamped to the signed leaf)"]
    E --> F["signed leaves + structured JSONL session log<br/>(finding -> tool execution -> leaf)"]

Full write-up and the architectural-vs-prompt boundary table: docs/ARCHITECTURE.md.

Quickstart (no API key, deterministic policy driver)

# 1. Install the engine (pre-existing component) and this agent.
pip install "git+https://github.com/javierdejesusda/logflip-closed"
pip install -e .            # from this repo root

# 2. Build the synthetic demo case (two journaled stomps + one anomaly).
python cases/demo_stomp/generate.py

# 3. Run the agent. It scans, investigates, and self-corrects on the anomaly.
python -m sift_agent --image cases/demo_stomp/case.img --usnjrnl-record 42 \
    --log logs/session.jsonl --leaf-dir cases/demo_stomp/leaves

Expected: records 5 and 7 report provisional (with signed leaves), record 12 reports anomaly (corroborated, then honestly not escalated). Exit code 2. The full reasoning and tool trace is written to logs/session.jsonl.

A pre-generated sample trace (deterministic policy driver) is committed at logs/sample_session.jsonl. A second sample from the OpenAI driver, logs/sample_session_openai.jsonl, shows the same triage with per-turn token usage and the model's own reasoning.

Try it out (Docker, one command)

docker compose up --build

This builds an image with the engine and the agent installed, generates the demo case, runs the agent, and prints the triage report plus the session-log path.

LLM-driven mode (real autonomous reasoning)

# Anthropic (Claude)
export ANTHROPIC_API_KEY=sk-ant-...
python -m sift_agent --image cases/demo_stomp/case.img --usnjrnl-record 42 \
    --driver claude --model claude-sonnet-4-6 --log logs/session.jsonl

# OpenAI
export OPENAI_API_KEY=sk-...
python -m sift_agent --image cases/demo_stomp/case.img --usnjrnl-record 42 \
    --driver openai --model gpt-4o --log logs/session.jsonl

Same tools, same guards; the model chooses the sequence and narrates its reasoning, and each turn's token usage is recorded in the session log. A .env file holding the key is auto-loaded. Without a key, the LLM drivers fall back to the deterministic policy so the agent always runs for a judge.

Repository layout

Evidence integrity (never-false-confirm)

A confirmed verdict requires all four engine gates to pass simultaneously (complete $LogFile inversion, a signed fingerprint match at >= 0.85 confidence, and two independent failure-mode classes), a real engagement key, and a signed fingerprint DB. The demo key cannot produce a confirmed leaf by construction, so the demo's honest ceiling is provisional. Anomalies (no $LogFile coverage) can never be confirmed. See docs/ACCURACY_REPORT.md.

Tests

python -m pytest tests/ -q      # 42 agent/MCP tests

The underlying engine ships its own 809-test suite (run from the logflip-closed checkout).

Documentation

• Architecture and trust boundaries

• Project description

• Dataset documentation

• Accuracy report

License

MIT. The logflip engine is a separate, pre-existing MIT component reused here; the novel contribution of this repository is the autonomous MCP agent, the verdict guard, the self-correction loop, and the signed session log.