DOCS / SAFETY

Safety and threat model

Treat operational evidence and model output as untrusted data while preserving local control and honest truth state.

PHASE 1 · PRE-1.0PYTHON 3.11+EDIT ON GITHUB ↗

Security invariants

  • Deterministic local use requires no credential or network call.
  • A model response is never a confirmed resolution or an executable action.
  • High-risk or irreversible actions cannot enter core without human approval policy.
  • Verification must follow any future execution before recovery is confirmed.
  • No telemetry is exported by default.

Current controls

ThreatCurrent control
Prompt injectionEvidence is data; models receive no core execution tools.
Secret leakageConservative recursive redaction, environment-variable secret references, and explicit model opt-in.
Hostile YAMLsafe_load, strict schemas, and a one-MiB limit.
Oversized inputsTen-MiB CLI log limit; bounded evidence items, characters, and response bytes.
Model hallucinationSchema validation, evidence separation, deterministic fallback, unconfirmed truth.
Forged webhooksConstant-time HMAC, timestamp skew limits, strict delivery IDs, fail-closed replay guard.
Malicious pluginsMetadata-only discovery, manifest validation, default-denied sensitive authorities, explicit load policy.
Unapproved actuationNo core executor; proposals expire and carry execution_allowed=false.
Silent telemetryNo remote telemetry adapter or default export.
Hostile YAML structureAliases rejected; nesting depth capped at 64 nodes before model validation.
Supply-chain tamperingSPDX SBOMs, signed provenance attestations, reproducible-build comparison, artifact content validation, secret scanning, and Trusted Publishing.

A recorded security and authority review accompanies the Phase 1 release, and dependency and license review runs on every pull request.

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Do not disclose active vulnerabilities in a public issue. Follow the repository SECURITY.md process and provide impact, affected versions, reproduction details, and mitigation ideas privately.