安全检测基准工具

Surfaces the upstream metaharness-darwin security bench command. **This is
the upstream's own ADR-155 — Darwin Shield — and is the closest reference
implementation for ruflo's nightly self-learning security harness (#2417).**

Why this matters for ruflo's ADR-155

ruflo's ADR-155 proposes three learning loops (per-dimension confidence,
severity calibration, auto-fix bid). Loop A trains on accumulated
(finding, dimension, human_outcome) tuples — but the gradient signal is
only sound if the underlying detection mechanism converges on a known-good
corpus. Darwin Shield evolves exactly that mechanism on a 10-vuln/9-decoy
ground-truth set. Running this nightly gives us:

• Empirical floor: if Darwin Shield's champion can't reach

TPR=1/FPR=0 on the bench corpus, our Loop A's reward signal is noise.

• Drift detection: week-over-week champion fitness deltas surface

when the security landscape (or our mutator policy) shifts.

• Baseline diversity: the 4 baselines (B0–B3) give us 4 anchor

points to weight per-dimension confidence against.

Algorithm

Implementation: scripts/security-bench.mjs.

1. Run metaharness-darwin security bench --population N --cycles N [--seed S] from the

installed @metaharness/darwin (pin in _darwin.mjs; one-time cache install only if
no installed copy qualifies — never npx).

1. Default timeout = 3s × 19 evaluations × population × cycles + 30s overhead.

At default --population 2 --cycles 1 ≈ 144s; at --population 4 --cycles 3 ≈ 12 min.

1. Parse the markdown report — overall PASS/FAIL plus per-gate

pass/fail rows (gate examples: "TPR improvement ≥ 25% vs fixed",
"FPR reduction ≥ 40%", "Patch-test pass rate ≥ 80%", "Reproduction
success ≥ 90%", "Unsafe outputs = 0", "Cost increase ≤ 2× fixed",
"Beyond SOTA: champion statistically beats previous champion",
"Compounding: false-positive repeat-rate drop ≥ 35%").

1. Parse the baselines-vs-champion table (4 rows: fitness/TPR/FPR/patchPass/

repro/unsafe/cost per harness).

1. Emit structured JSON. With --alert-on-fail, exit 1 when overall = FAIL.

Output shape

{
  "success": true,
  "data": {
    "overall": { "ok": true, "icon": "✅" },
    "gates": {
      "total": 11,
      "passed": 11,
      "failed": 0,
      "details": [{ "ok": true, "criterion": "TPR improvement ≥ 25% vs fixed harness", "measured": "+150% (B2 0.4 → B3 1)" }, ...]
    },
    "baselines": [
      { "harness": "static-only", "fitness": 0.5665, "tpr": 0.3, "fpr": 1, "unsafe": 0, ... },
      { "harness": "LLM single-pass", "fitness": 0.1365, ... },
      { "harness": "fixed agent", "fitness": 0.598, ... },
      { "harness": "Darwin champion", "fitness": 0.93275, "tpr": 1, "fpr": 0, ... }
    ],
    "rawMarkdown": "...",
    "shape": { "population": 2, "cycles": 1, "seed": null },
    "durationMs": 142000
  }
}

Wiring into ADR-155 nightly harness

The ADR-155 nightly workflow (per #2418 task W1.5) will spawn this as
one of the active-pentest dimension's calls — its results become a
trajectory record:

{
  "dimension": "mcp-pentest",
  "subdimension": "darwin-shield-bench",
  "champion_fitness": 0.93275,
  "champion_tpr": 1, "champion_fpr": 0,
  "gates_passed": 11, "gates_failed": 0,
  "shape": { "population": 4, "cycles": 3 }
}

Loop A learns: if darwin-shield-bench consistently passes on the seeded
corpus, weight findings caught only by mcp-pentest higher.

Exit codes

Graceful degradation

When @metaharness/darwin is absent, emits {degraded: true, reason: 'metaharness-darwin-not-available'} and exits 0.