Publish Layerfault synthetic corpus LF-CORPUS-NPY-0001

LAYERFAULT_CORPUS.json

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+{
+  "candidate_rules": [],
+  "corpus_id": "LF-CORPUS-NPY-0001",
+  "description": "NumPy object array plus explicit allow_pickle loader call.",
+  "expected_rules": [
+    "LF-NPY-PICKLE",
+    "LF-PY-NUMPY-ALLOW-PICKLE",
+    "LF-CORR-NUMPY-ALLOW-PICKLE"
+  ],
+  "generator": "formats.npy_pickle",
+  "ground_truth": "synthetic-generated",
+  "must_not_rules": [],
+  "repo_name": "numpy-object-pickle",
+  "repo_type": "model",
+  "safety": "inert/canary-only",
+  "schema": 1,
+  "validation": {
+    "kind": "verify-package",
+    "target": "."
+  }
+}

README.md

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+---
+license: apache-2.0
+tags:
+- layerfault
+- security-research
+- model-security
+- synthetic
+- adversarial-testing
+extra_gated_prompt: >-
+  This repository is a synthetic security-test artifact from the Layerfault
+  corpus. It intentionally contains adversarial characteristics (e.g.
+  suspicious pickle opcodes, executable-format smuggling, prompt-injection
+  strings) designed to exercise security scanner detection rules. It is
+  **not** a usable ML model and must never be loaded or executed outside an
+  isolated scanner-testing environment. By accepting, you confirm you
+  understand this repository is a test fixture, not production model
+  weights.
+extra_gated_button_content: I understand this is a security test fixture and accept the risk
+gated: auto
+---
+
+# numpy-object-pickle
+
+> **SECURITY TEST ARTIFACT — DO NOT USE AS A PRODUCTION MODEL**
+
+This repository is part of the Layerfault synthetic security corpus.
+It is deliberately constructed to contain security-relevant characteristics for scanner testing.
+
+**Corpus ID:** `LF-CORPUS-NPY-0001`
+
+## Purpose
+
+NumPy object array plus explicit allow_pickle loader call.
+
+## Direct expected Layerfault rules
+
+- `LF-NPY-PICKLE`
+- `LF-PY-NUMPY-ALLOW-PICKLE`
+- `LF-CORR-NUMPY-ALLOW-PICKLE`
+
+## Candidate rules
+
+These are deliberately plausible targets that remain marked as candidates until the exact
+Layerfault build used for certification confirms them.
+
+- None
+
+## Negative-control rules
+
+These should remain silent for this corpus item.
+
+- None
+
+## Safety
+
+The corpus uses fake secrets, loopback/`.invalid` network destinations, harmless marker output,
+and synthetic model behavior only. It is intended for static scanning and isolated security testing.

SHA256SUMS.json

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+[
+  {
+    "path": "LAYERFAULT_CORPUS.json",
+    "sha256": "294659fc5c0e45f78bfdeabfdb485fe898a976c682f2c966c18e871aad52f83f",
+    "size": 540
+  },
+  {
+    "path": "README.md",
+    "sha256": "643f0d41c50c6a437eb488eaea6af3b80c103cb274e5ca31f2dcce2a0394fb00",
+    "size": 1715
+  },
+  {
+    "path": "loader.py",
+    "sha256": "e30d75e76e9c768776abf056090b743eed234d8cd7f971cd1c241b704258e4af",
+    "size": 60
+  },
+  {
+    "path": "object.npy",
+    "sha256": "0948fcadec103509006c9ef113df26b0800c37fe3210bc5d80bbd1bfe0b28035",
+    "size": 324
+  }
+]

loader.py

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+import numpy as np
+np.load('object.npy', allow_pickle=True)

object.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0948fcadec103509006c9ef113df26b0800c37fe3210bc5d80bbd1bfe0b28035
+size 324