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@@ -1,44 +1,138 @@
----
-license: apache-2.0
-dataset_info:
-  features:
-  - name: text
-    dtype: large_string
-  - name: label
-    dtype: int64
-  - name: source
-    dtype: large_string
-  - name: attack_type
-    dtype: large_string
-  splits:
-  - name: train
-    num_bytes: 88567379
-    num_examples: 19895
-  - name: validation
-    num_bytes: 19714397
-    num_examples: 4263
-  - name: test
-    num_bytes: 18512400
-    num_examples: 4265
-  - name: adversarial_200
-    num_bytes: 1605924
-    num_examples: 200
-  - name: skillsbench_infile
-    num_bytes: 12382969
-    num_examples: 1541
-  download_size: 72378443
-  dataset_size: 140783069
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
-  - split: adversarial_200
-    path: data/adversarial_200-*
-  - split: skillsbench_infile
-    path: data/skillsbench_infile-*
----
+---
+language:
+- en
+license: apache-2.0
+task_categories:
+- text-classification
+tags:
+- security
+- prompt-injection
+- llm-security
+- agent-skills
+- agentic-ai
+- roberta
+- cybersecurity
+- vulnerability-detection
+- benchmark
+dataset_info:
+  features:
+  - name: text
+    dtype: string
+  - name: label
+    dtype: int64
+  - name: source
+    dtype: string
+  - name: attack_type
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 19880
+  - name: validation
+    num_examples: 4260
+  - name: test
+    num_examples: 4260
+  - name: adversarial_200
+    num_examples: 200
+  - name: skillsbench_infile
+    num_examples: 1541
+size_categories:
+- 10K<n<100K
+---
+
+# 🛡️ SkillGuard v2: Complete Dataset & Adversarial Benchmarks Suite
+
+**SkillGuard v2** is a curated dataset and benchmarking suite designed for training, evaluating, and stress-testing machine learning systems that detect **hidden prompt injections, credential exfiltration, and privilege escalation payloads in LLM Agent Skills (`SKILL.md`)**.
+
+- **Domain:** LLM Agent Security, Indirect Prompt Injection, Adversarial Robustness
+- **Language:** English & Software Code (Markdown, Bash, Python, YAML, Swift)
+
+---
+
+## 📊 Dataset & Benchmark Splits Summary
+
+This repository includes both the **core training/testing splits** and **two independent held-out evaluation benchmarks**:
+
+| Split Name | Samples | Benign (0) | Malicious (1) | Description |
+| :--- | :--- | :--- | :--- | :--- |
+| **`train`** | 19,880 | 9,940 (50.0%) | 9,940 (50.0%) | Core training set combining SkillMD-138k and synthetic threats. |
+| **`validation`** | 4,260 | 2,130 (50.0%) | 2,130 (50.0%) | Hyperparameter tuning and checkpoint selection split. |
+| **`test`** | 4,260 | 2,130 (50.0%) | 2,130 (50.0%) | In-distribution held-out test split. |
+| **`adversarial_200`** | 200 | 100 (50.0%) | 100 (50.0%) | **Adversarial Zero-Day Benchmark**: 12 brand-new, unseen attack patterns injected into held-out skills. |
+| **`skillsbench_infile`** | 1,541 | 1,500 (97.3%) | 41 (2.7%) | **SkillsBench-1650 Benchmark**: External benchmark testing in-file prompt injections across 8 categories. |
+
+---
+
+## 🧬 Threat Taxonomy & Attack Vectors
+
+### 1. Synthetic Training Taxonomy (8 Categories in `train` / `validation` / `test`)
+- **Data Exfiltration:** Hidden `curl`/`wget`/`nc` commands stealing SSH keys, AWS credentials, or tokens.
+- **Guardrail Override:** Authority deception (claiming automated pre-approval or disabling confirmation dialogs).
+- **Encoded Payloads:** Base64/Hex/rot13 encoded shell commands executed via `eval` or Python `base64.b64decode`.
+- **Instruction Reprogram:** Prompt hijacking overriding the agent's core identity and role instructions.
+- **Obfuscated Shell:** Variable expansion and subshell nesting to disguise dangerous binaries.
+- **Dependency Poisoning:** Coercing agents to install malicious packages from untrusted third-party registries.
+- **Hidden Comments:** Embedding injection commands inside HTML comments (`<!-- ... -->`) or markdown metadata.
+- **Persistence / Backdoors:** Appending malicious aliases or cronjobs to shell configuration files.
+
+### 2. Unseen Zero-Day Vectors (Tested in `adversarial_200`)
+Tests out-of-distribution generalization on novel attack phrasings:
+1. Fake GDPR Article 17 Data Portability compliance exfiltration.
+2. Open Source License verification telemetry.
+3. Automated Security Scanner access verification manipulation.
+4. Skill Registry auto-update override.
+5. OpenTelemetry distributed tracing exfiltration.
+6. Anonymized usage analytics tarball exfiltration.
+7. Jira elevation override authorization deception.
+8. Internal pentest privileged mode flags.
+9. Encoded Base64 runtime dependency bootstrapping.
+10. Encoded preflight handshake verification.
+11. SOC-2 compliance audit telemetry mandates.
+12. Verified Publisher digital signature verification hooks.
+
+---
+
+## 🚀 Quickstart: Loading Datasets & Benchmarks
+
+```python
+from datasets import load_dataset
+
+# 1. Load the entire suite
+dataset = load_dataset("ZAHRA585/skillguard-v2-dataset")
+
+print(dataset)
+# Output:
+# DatasetDict({
+#     train: Dataset({features: ['text', 'label', 'source', 'attack_type'], num_rows: 19880}),
+#     validation: Dataset({features: ['text', 'label', 'source', 'attack_type'], num_rows: 4260}),
+#     test: Dataset({features: ['text', 'label', 'source', 'attack_type'], num_rows: 4260}),
+#     adversarial_200: Dataset({features: ['text', 'label', 'source', 'attack_type'], num_rows: 200}),
+#     skillsbench_infile: Dataset({features: ['text', 'label', 'source', 'attack_type'], num_rows: 1541})
+# })
+```
+
+---
+
+## 📁 Schema
+
+| Column | Type | Description |
+| :--- | :--- | :--- |
+| `text` | `string` | Full content of the skill file or injected prompt block. |
+| `label` | `int64` | Binary label: `0` for Benign, `1` for Malicious. |
+| `source` | `string` | Origin split or benchmark name (`skillmd_138k`, `synthetic_v2`, `custom_adversarial_injection`, `skillsbench_infile`, etc.). |
+| `attack_type` | `string` | Specific threat category (e.g. `data_exfil`, `unseen_injection`, `plaintext_cmd`, `none`). |
+
+---
+
+## 📜 Citation
+
+If you use this dataset, its adversarial benchmarks, or the SkillGuard v2 model, please cite:
+
+```bibtex
+@misc{marouf2026skillguard,
+  author = {Marouf, Zohra and Bousmaha, R.},
+  title = {SkillGuard v2: Transformer-Based Detection of Semantic Prompt Injections in LLM Agent Skills Under Adversarial Distribution Shifts},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/datasets/ZAHRA585/skillguard-v2-dataset}},
+}
+```