Publish Layerfault synthetic corpus LF-CORPUS-LORA-0001

LAYERFAULT_CORPUS.json

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+{
+  "candidate_rules": [
+    "LF-ADAPTER-SCALING-ANOMALY",
+    "LF-ADAPTER-MODULES-TO-SAVE",
+    "LF-ADAPTER-NORM-OUTLIER",
+    "LF-ADAPTER-SPECTRAL-CONCENTRATION",
+    "LF-ADAPTER-BASE-UNVERIFIED",
+    "LF-ADAPTER-UNEXPECTED-MODULE"
+  ],
+  "corpus_id": "LF-CORPUS-LORA-0001",
+  "description": "One tiny LoRA adapter combining rank, scaling, modules_to_save, norm, spectral and weight-concentration anomalies.",
+  "expected_rules": [
+    "LF-ADAPTER-RANK-ANOMALY",
+    "LF-ADAPTER-WEIGHT-ANOMALY"
+  ],
+  "generator": "lora.lora_anomaly",
+  "ground_truth": "synthetic-generated",
+  "must_not_rules": [],
+  "repo_name": "lora-anomaly-suite",
+  "repo_type": "model",
+  "safety": "inert/canary-only",
+  "schema": 1,
+  "validation": {
+    "kind": "manual",
+    "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
+---
+
+# lora-anomaly-suite
+
+> **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-LORA-0001`
+
+## Purpose
+
+One tiny LoRA adapter combining rank, scaling, modules_to_save, norm, spectral and weight-concentration anomalies.
+
+## Direct expected Layerfault rules
+
+- `LF-ADAPTER-RANK-ANOMALY`
+- `LF-ADAPTER-WEIGHT-ANOMALY`
+
+## Candidate rules
+
+These are deliberately plausible targets that remain marked as candidates until the exact
+Layerfault build used for certification confirms them.
+
+- `LF-ADAPTER-SCALING-ANOMALY`
+- `LF-ADAPTER-MODULES-TO-SAVE`
+- `LF-ADAPTER-NORM-OUTLIER`
+- `LF-ADAPTER-SPECTRAL-CONCENTRATION`
+- `LF-ADAPTER-BASE-UNVERIFIED`
+- `LF-ADAPTER-UNEXPECTED-MODULE`
+
+## 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": "a07572a424ebfead6c172ce07e822e6cbc813840308ed196f85b1e721461b987",
+    "size": 776
+  },
+  {
+    "path": "README.md",
+    "sha256": "65e6e7e9ca793384c4b64ead49dfbd66fb754a9402592e08ba88f8ecf7eaa656",
+    "size": 1936
+  },
+  {
+    "path": "adapter_config.json",
+    "sha256": "3d441bba94fd99884ae4d0a64ec375d9f3a7412c0472a71ef96fa20d2f77885b",
+    "size": 285
+  },
+  {
+    "path": "adapter_model.safetensors",
+    "sha256": "5c7353cd80b25d55c67ec0396a907c2218ccbc4f662adf2ea89f8ccbef72cb16",
+    "size": 584
+  }
+]

adapter_config.json

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+{
+  "base_model_name_or_path": "layerfault/nonexistent-base",
+  "bias": "none",
+  "lora_alpha": 1000000000.0,
+  "modules_to_save": [
+    "lm_head"
+  ],
+  "peft_type": "LORA",
+  "r": 5000,
+  "target_modules": [
+    "layer0",
+    "layer1",
+    "layer2"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5c7353cd80b25d55c67ec0396a907c2218ccbc4f662adf2ea89f8ccbef72cb16
+size 584