v1.1.0 — Stable release (SANS FIND EVIL! 2026)

Agentic-DART is an autonomous DFIR agent on the SANS SIFT Workstation. It runs a senior-analyst reasoning loop over a custom MCP server of 73 typed, read-only forensic tools (48 native pure-Python + 25 SIFT adapters) and produces a courtroom-traceable report. Evidence integrity is enforced by the shape of the system — destructive operations (execute_shell, write_file, mount) simply do not exist on the wire — not by asking the model to behave.

This is the first genuinely stable release, verified end-to-end from a clean clone.

Why 1.1.0 supersedes everything before it

Earlier tags that claimed "stable" did not actually run clean in a fresh environment. 1.1.0 is the result of a full correctness pass — install, benchmark, scoring, external disk-image handling, and the test suite all fixed and re-verified. The prior 1.0.2 "stable" tag has been removed to avoid confusion.

• Tests are green from anywhere. 156 tests pass. The Phase-2 placeholder suite is now explicitly skipped (not failed) wherever it's collected, so pytest is clean whether you run it from the repo root or any subdirectory.
• No version is pinned in docs or tests. The release number lives in pyproject.toml only; READMEs, the wiki, the site, and the version test were all genericized, so a future bump touches one file.

Highlights

• 73 typed read-only MCP tools — 48 native forensic functions + 25 SIFT adapters (Volatility 3, MFTECmd, EvtxECmd, PECmd, RECmd, AmcacheParser, YARA, Plaso), plus a versioned Sigma detection-rule matcher.
• 11 case studies / 99 ground-truth findings across two tiers: 8 internal self-evaluation cases (ready evidence) + 3 external full-disk public images (NIST CFReDS Hacking Case, Ali Hadi DFIR Challenge #1, Digital Corpora M57).
• External is a first-class tier. Full-disk images are adapted via ewfmount + mmls + tsk_recover (partition-offset aware) into an evidence tree, then analyzed. Run the tiers as separate processes — scripts.eval.demo / scripts.eval.self / scripts.eval.external — each independently debuggable; an append-only docs/benchmarks/HISTORY.md records every self/external run.
• Linux-only host, hardened installer — refuses to run under sudo, stages the full toolchain, verifies it, and offers to fetch the external images at the end.

Requirements / dependencies

Host OS — Linux only. Verified on the SANS SIFT Workstation (Ubuntu 22.04); RHEL / Rocky / AlmaLinux 8+ and Fedora work via dnf/yum. macOS and Windows are not supported as the host — the Plaso / libyal toolchain does not build cleanly there. Default shell is bash.

Requirement	Version	Verified
Python	3.10+ (CI: 3.10 – 3.13)	3.10, 3.12
OS	Ubuntu 22.04 (SANS SIFT) primary; RHEL/Rocky/Alma 8+, Fedora	SIFT

Python libraries (lower bounds; installed by scripts/install.sh):

Library	Minimum	Role
anthropic	≥ 0.40	Claude API client (live mode)
mcp	≥ 1.0	MCP client/server transport
duckdb	≥ 1.5.3, < 2.0	in-memory correlation store
python-registry	≥ 1.3	Windows registry hive parsing
PyYAML	≥ 6.0	playbook / Sigma rule loading
requests	≥ 2.25	dataset download (benchmarks)

External forensic tools (staged by the installer; SIFT ships most): sleuthkit (mmls, tsk_recover), ewfmount (ewf-tools / libewf), Volatility 3, Plaso (log2timeline.py, psort.py), EZ Tools, YARA, Velociraptor.

Install

git clone https://github.com/Juwon1405/agentic-dart.git
cd agentic-dart
bash scripts/install.sh          # Linux only; refuses sudo. Offers to fetch external images (~13 GB).
export ANTHROPIC_API_KEY='sk-ant-...'
python3 -m scripts.eval.demo                                           # deterministic, no key
python3 -m scripts.eval.self     --models claude-haiku-4-5-20251001   # 8 bundled cases
python3 -m scripts.eval.external --models claude-haiku-4-5-20251001   # public disk images

License: MIT. SANS FIND EVIL! 2026 submission.