basalt-scan 0.1.1

A purple-team of AI agents that audits code security and proves findings by execution.

Basalt 🛡️🔴🔵🟣

A purple-team of AI agents that audits code security and proves findings by execution — not by opinion.

Most "AI security scanners" hand you a list of maybe-bugs a model thinks it found. Basalt is different: a finding is only confirmed when its proof-of-concept is reproduced by execution in a clean, isolated sandbox (or matched against an authoritative vulnerability database). What you get is a short list of things that actually reproduce — with the evidence.

recon → red (find × N) → purple (verify) → EXECUTION in sandbox → blue (patch) → grader
        └ consensus voting          └ skeptical judge      └ sentinel must fire

Screening, not a substitute for a professional manual audit of code holding real funds. Based on the 2024–2026 evidence (Google Big Sleep, Anthropic's defending-code harness, LLM-SmartAudit): what actually cuts false positives is reproducing the PoC by execution, not model-vs-model debate.

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Why Basalt

• Execution-verified findings. A candidate is confirmed only if its PoC fires the BASALT-POC-TRIGGERED sentinel in a network-less Docker sandbox. Every finding carries honest provenance: tool-proven, osv-match, pattern-match, or model-asserted.
• A verification funnel in every report — "N candidates → rejected by judge → not reproduced → confirmed" — so you can see the filtering, not just the conclusions.
• Deterministic scanners that just work (stable): dependency N-day vs OSV.dev, Go reachability via govulncheck, and hardcoded-secret detection (never stores the secret value).
• Multi-language: Python, Go, TypeScript/JS, Rust, Solidity, generic.
• Built for CI: severity-gated exit codes, PR-only --changed audits, cost --dry-run, and reports as Markdown/JSON/HTML + a professional Word/PDF audit document with an evidence bundle.
• Pluggable models: Claude (subscription CLI or API), any OpenAI-compatible gateway, or local Ollama — per role, with fallback chains.

⚠️ Maturity. The deterministic scanners (N-day, reachability, secrets) are stable. The LLM discovery path (finding logic bugs via the red agent) is beta — verified-by-execution, but coverage and precision are still maturing. See Known limitations.

Install

pipx install "basalt-scan[claude,doc]"   # recommended — installs the `basalt` command
# or: pip install "basalt-scan[claude,doc]"
basalt init                              # writes config/basalt.yaml

The distribution is basalt-scan; the command and import package are basalt. Extras: claude (Anthropic SDK), doc (Word/PDF reports), mcp (MCP server), sandbox (Docker SDK).

Docker is required for execution-based verification. govulncheck is optional (Go reachability). The Word/PDF report uses WeasyPrint, which needs system libs (pango, cairo).

From source (development)

git clone https://github.com/Arkessiah/Basalt && cd Basalt
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev,claude,doc]"

Usage

basalt doctor                       # providers + Docker + govulncheck health
basalt audit ./repo --deps --patch --report out.md    # full audit
basalt audit ./repo --dry-run                         # estimate cost (no model calls)
basalt audit ./repo --changed --base main             # PRs: only changed files
basalt audit ./repo --fail-on high                    # CI: non-zero exit on high+ findings

basalt deps ./repo --reachability   # dependency N-day (OSV) + Go reachability
basalt secrets ./repo               # hardcoded secrets (cross-language)

basalt report --format html -o report.html            # markdown | json | html
basalt doc ./repo -o audit                            # Word + PDF proof document
basalt bundle ./repo -o evidence.zip                  # report + PoCs + logs + hash
basalt gate --fail-on high                            # CI gate over the findings store
basalt mcp-serve                    # MCP server: drive audits from an MCP client

Report/document language is English by default; set lang: es in the config for Spanish (extensible to more languages).

How it works

1. Recon maps the attack surface and prioritizes partitions by risk.
2. Red (find × N) independent agents look for vulnerabilities and produce a runnable PoC; consensus voting filters hallucinations.
3. Purple (verify) is a skeptical judge that demands a credible PoC.
4. Execution reproduces the PoC in a clean, isolated Docker sandbox — the finding is confirmed only if the exploit actually fires.
5. Blue (patch) + Grader propose a minimal fix and grade it by execution: green build, closed PoC, no test regressions.

Untrusted target content is spotlighted and secret-redacted before it reaches any model, and the model agents never invoke tools directly — the deterministic orchestrator mediates all execution.

Layout: providers/ models · agents/ roles · orchestrator.py the loop · sandbox/ isolated execution · targets/ language adapters · deps.py / reachability.py / secrets_scan.py deterministic scanners · report.py / audit_doc.py output · role prompts in prompts/*.md.

Known limitations

• LLM discovery is beta — execution-verified, but not yet a replacement for a thorough manual review; the deterministic scanners are the solid part today.
• Reachability is Go-only (govulncheck); other ecosystems report unknown, so an N-day may be present but not exercised.
• Solidity PoC reproduction needs forge-std available in the target project.
• Compiled-language PoCs must be self-contained (the sandbox has no network).

Security & ethics

Basalt is a defensive tool: audit code/infrastructure you own or are authorized to test. It executes PoCs and target code inside an isolated sandbox. See SECURITY.md for authorized-use and responsible disclosure. You are responsible for how you use it.

Contributing

See CONTRIBUTING.md. CI runs ruff + pytest on Python 3.11/3.12.

License

MIT — © 2026 Arkessiah. See LICENSE.