FIND EVIL! — SIFT Forensic AI Agent

Autonomous incident response agent that mounts a 119 GB forensic disk image, hunts malware and anti-forensics through 18 MCP tools on a SIFT Workstation VM, and writes a courtroom-ready report — with no human in the loop.

Demo video: https://youtu.be/ySjuSR9AP3Q
License: MIT
Architecture pattern: Custom MCP Server

---

What it does

The agent receives a single prompt ("investigate the VANKO disk image") and autonomously:

1. Mounts the EWF forensic image read-only via ewfmount + ntfs-3g
2. Enumerates users, recent files, and installed software
3. Scans for suspicious executables in %TEMP%, %AppData%, and Downloads
4. Parses the Windows registry for persistence mechanisms
5. Extracts and correlates Windows Event Log logon events
6. Runs YARA malware signatures across the image
7. Identifies Prefetch artifacts proving anti-forensic tool execution
8. Produces findings/findings_report.json with confidence-scored IOCs

On the VANKO case it found 8 confirmed findings including WiFi packet capture, evidence destruction (SDelete), an encrypted volume (VeraCrypt FORMAT confirmed), a typosquatted RAT, and identified the subject as anthony.vanko@gmail.com.

---

Architecture

┌──────────────────────────────────────────────────────────┐
│  Windows Host (analyst workstation)                       │
│                                                           │
│  orchestrator.py  ←→  gpt-5.4-mini (OpenAI-compat API)   │
│        │                                                  │
│  sift-forensic-mcp  (18 MCP tools, stdio transport)       │
│        │  asyncssh (TCP 22)                               │
└────────┼─────────────────────────────────────────────────┘
         │
┌────────▼─────────────────────────────────────────────────┐
│  SIFT Workstation 2026 VM  (Ubuntu 22.04, VMware NAT)     │
│                                                           │
│  /cases/VANKO/surface_physical.E01                        │
│       ewfmount → /mnt/ewf/ewf1                            │
│       kpartx   → /dev/mapper/loop0p3                      │
│       ntfs-3g  → /mnt/windows/  (READ-ONLY)               │
│                                                           │
│  SIFT tools: ewfmount, log2timeline, yara,                │
│              regripper, python-evtx, strings, file        │
└──────────────────────────────────────────────────────────┘

See docs/architecture.md for the full tool inventory and security boundary breakdown.

---

Prerequisites

• Windows 10/11 host with VMware Workstation Pro 17+
• Python 3.10+
• OpenAI-compatible API key (or set OPENAI_BASE_URL to a local endpoint)
• ~150 GB free disk space (119 GB evidence + SIFT VM)
• 8 GB+ RAM (16 GB recommended)

---

Quick start

1. Clone and install

git clone https://github.com/OLGTX303/find-evil-sift-agent
cd find-evil-sift-agent
pip install -e .

2. Import the SIFT Workstation VM

$ovftool = "C:\Program Files (x86)\VMware\VMware Workstation\OVFTool\ovftool.exe"
& $ovftool --acceptAllEulas --name="SIFT-2026" sift-2026-04-22.ova F:\SIFT-VM\

3. Place evidence files

find\VANKO\surface_physical.E01  (through .E21)
find\VANKO\vanko-c-drive.CYLR.7z

4. Start and configure the SIFT VM

python setup_sift_vm.py
# Starts the VM, enables SSH, copies evidence — prints the VM IP at the end

5. Set environment variables

$env:OPENAI_API_KEY    = "sk-..."
$env:OPENAI_BASE_URL   = "https://api.openai.com/v1"   # or your endpoint
$env:SIFT_HOST         = "192.168.x.x"   # from setup_sift_vm.py
$env:SIFT_PORT         = "22"
$env:SIFT_USER         = "sansforensics"
$env:SIFT_PASS         = "forensics"
$env:EVIDENCE_DIR      = "/cases/VANKO"

6. Run the investigation

python orchestrator.py --output-dir ./findings

The agent prints reasoning and tool calls to stderr in real time.
Investigation takes 15–30 minutes (log2timeline on 119 GB runs in background).

7. Review results

# Structured findings report
cat findings/findings_report.json

# Full timestamped audit trail
cat findings/agent_execution_log.jsonl

---

MCP server (standalone — use with Claude Code)

# Register the MCP server in Claude Code
claude mcp add sift-forensic \
  -e SIFT_HOST=192.168.x.x \
  -e SIFT_PORT=22 \
  -e SIFT_USER=sansforensics \
  -e SIFT_PASS=forensics \
  -- sift-mcp

# Then in Claude Code:
# "Mount the VANKO image and find evil"

---

Repository layout

sift-agent/
├── orchestrator.py          ← Autonomous IR agent (gpt-5.4-mini)
├── setup_sift_vm.py         ← One-time VM setup
├── pyproject.toml
├── LICENSE                  ← MIT
├── src/sift_mcp/
│   ├── server.py            ← MCP server (stdio transport)
│   ├── tools.py             ← 18 forensic tool implementations
│   └── ssh_client.py        ← asyncssh helper with sudo support
├── findings/
│   ├── findings_report.json         ← Structured IOC report
│   └── agent_execution_log.jsonl    ← Full timestamped audit trail
├── demo/
│   ├── demo_find_evil.mp4           ← Narrated demo video (local copy)
│   ├── mcp_session.json             ← Real captured tool output
│   └── cover_3x2.png                ← Devpost thumbnail (1200×800)
└── docs/
    ├── architecture.md      ← Component diagram + security boundaries
    ├── accuracy_report.md   ← Finding accuracy + false positive analysis
    ├── dataset.md           ← Evidence dataset documentation
    └── try-it-out.md        ← Judges guide

---

Docs

Document	Contents
docs/architecture.md	Component diagram, tool inventory, security boundaries, guardrails
docs/accuracy_report.md	8 findings vs ground truth, false positives, evidence integrity
docs/dataset.md	VANKO case dataset, provenance, integrity hashes
docs/try-it-out.md	Step-by-step judges guide with troubleshooting

---

License

MIT — see LICENSE.