HEEL v1.1.0

HEEL v1.1.0 — agent-native abuse-simulation tool. Rehearse how a customer could abuse your product before it ships.

pip install heel-sim · pure stdlib, zero dependencies · MCP-first · Apache-2.0

Highlights

• Apache-2.0 relicense + DCO — enterprise-friendly patent grant; clean contribution provenance.
• Research scenario library — 45 source-anchored business-logic abuse scenarios across all 10 categories (OWASP API/OAT/WSTG/LLM-Top-10, the MCP 2025-06-18 schema, Stripe/Kong/Microsoft/Auth0 config vocab). Library 67 → 119 scenarios.
• +12pp honest recall lift: on the frozen, independently-LLM-authored held-out test set, localization recall 0.38 → 0.50 at precision 0.97 → 0.98 — measured against abuse vocabulary HEEL never saw.

Assurance

Four adversarial red-team passes (all findings fixed) · OpenSSF Scorecard + CodeQL · Sigstore-signed release provenance + SBOM · 53 tests on Python 3.11–3.13. See TRUST.md / SECURITY.md.

Full notes: CHANGELOG.md.

HEEL v1.0.0

Changelog

All notable changes to HEEL are documented here. Format loosely follows Keep a Changelog.

[1.0.0] — 2026-06-05

First production-ready release: an agent-native abuse-simulation tool whose canonical surface is an
MCP server, proven by an honest detection metric on independently-authored targets.

Core capability

• MCP server (heel-mcp, stdio JSON-RPC) exposing 8 consumption/execution tools — no
  scope-mutation tool exists, by construction. Thin REST API (heel-rest) and CLI (heel)
  over the same capability.
• Out-of-band, HMAC-signed, immutable authorization scopes (confused-deputy model). Every
  caller-side escalation is rejected and written to an HMAC-hash-chained, tamper-evident containment
  log. (TestAuthGate, TestScopeImmutability.)
• Two agent classes: adversarial (declarative, model-driven) and opportunistic-human
  (motivation-profiled). Affordance chaining for multi-step abuse.
• Declarative scenario library across all 10 abuse categories, addable without code (incl. JSON);
  semantic signal matching for vocabulary generalization. Swappable LLM control loop
  (HEEL_MODEL=anthropic, via stdlib urllib) with a deterministic offline default.
• Control search, optional off-by-default data-classification annotation, lane-discipline handoffs.

Honest evaluation (the spine)

• Planted-vector self-consistency backtest on two synthetic targets (labeled as a wiring metric).
• Blind-target evaluation (independent encodings) with measured encoding-overlap + Wilson CI.
• Held-out evaluation against targets authored by an independent LLM swarm, blind to the probe
  vocabulary, with a dev/test split (test set frozen + content-hashed):
  • localization recall 0.38 (cluster-CI [0.29, 0.49]), attribution recall 0.31, precision
    0.97 on 199 independently-authored weaknesses.
  • Two gaps disclosed, not hidden: dev→test (overfitting) and localization→attribution (mis-categorization).
• Four adversarial red-team passes (safety spine, blind-eval honesty, held-out methodology, and a
  production launch-readiness security review — verdict SHIP, no blockers); all findings fixed,
  including REST anti-DNS-rebinding + anti-CSRF and data-dir 0700 enforcement. See docs/REDTEAM_*.md.

Tooling & ops

• Control-room UI (web/, Next.js) — abuse board, backtest, blind/held-out eval, live swarm,
  auth gate, scope panel, containment log, MCP/integration, scenario library.
• pip install heel (pure-stdlib, zero runtime deps); console scripts heel / heel-mcp /
  heel-rest; heel doctor self-check; heel eval honest headline.
• GitHub Actions CI (Python 3.11–3.13 + wheel smoke test + UI build). 53 tests.

Safety (§10, non-negotiable)

Synthetic-first · contained canary-only PoCs · never generates prohibited content · no real-PII ·
plausibility-weighted · severity-honest · immutable self-audit · lane discipline. See SECURITY.md.