Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- tom-gibbs/multi-turn_jailbreak_attack_datasets
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- microsoft/deberta-v3-small
+tags:
+- cybersecurity's
+- AI
+- JAILBROK-DETECTOR
+---
+
+# BLACKCELL-VANGUARD-v1.0-guardian
+
+> **Codename**: *Guardian of Safe Interactions*
+> **Model Lineage**: [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small)
+> **Author**: [SUNNYTHAKUR@darkknight25](https://huggingface.co/darkknight25)
+
+---
+
+## 🧭 Executive Summary
+
+**BLACKCELL-VANGUARD-v1.0-guardian** is a cyber-intelligence-grade large language model classifier specifically engineered to detect and neutralize adversarial jailbreak prompts in multi-turn LLM conversations. Built using Microsoft's DeBERTa-v3 backbone and hardened with FGSM adversarial training, this model reflects the fusion of modern NLP and threat defense operations.
+
+---
+
+## 🔒 Purpose
+
+> To detect and flag malicious prompts designed to jailbreak or bypass safety protocols in generative AI systems.
+
+**Use Cases:**
+
+* LLM Firewalling & Pre-filtering
+* Threat Simulation in AI Systems
+* AI Red Teaming / Prompt Auditing
+* Content Moderation Pipelines
+* Adversarial Robustness Benchmarking
+
+---
+
+## 🧠 Architecture
+
+| Component           | Description                                          |
+| ------------------- | ---------------------------------------------------- |
+| Base Model          | `microsoft/deberta-v3-small`                         |
+| Task                | Binary Sequence Classification (Safe vs Jailbreak)   |
+| Classification Head | Linear Layer with Softmax                            |
+| Adversarial Defense | FGSM (Fast Gradient Sign Method) on Input Embeddings |
+| Tokenizer           | SentencePiece + WordPiece Hybrid (SPM)               |
+
+---
+
+## 🛠️ Training Pipeline
+
+### 1. Dataset Curation
+
+* Source: [tom-gibbs/multi-turn\_jailbreak\_attack\_datasets](https://huggingface.co/datasets/tom-gibbs/multi-turn_jailbreak_attack_datasets)
+* Labeling Logic:
+
+  * `label = 1` if any of `Jailbroken['Multi-turn'] > 0` or `['Single-turn'] > 0`
+  * `label = 0` for safe or benign prompts
+* Static Safe Prompts appended for balance
+
+### 2. Preprocessing
+
+* Tokenization: max length 128 tokens
+* Augmentation: WordNet synonym substitution (50% prompts)
+
+### 3. Adversarial Training
+
+* Applied FGSM on embeddings
+* `ε = 0.1` for gradient-based perturbations
+
+### 4. Training Setup
+
+* Epochs: 3
+* Batch Size: 16
+* Optimizer: AdamW, LR=2e-5
+* Split: 70% Train / 15% Val / 15% Test
+
+---
+
+## 📊 Performance Report
+
+### Evaluation Metrics (on hold-out test set):
+
+| Metric    | Score |
+| --------- | ----- |
+| Accuracy  | 1.00  |
+| Precision | 1.00  |
+| Recall    | 1.00  |
+| F1-Score  | 1.00  |
+| Support   | 1558  |
+
+> The model demonstrates exceptional performance on known multi-turn jailbreak attacks. Real-world generalization advised with ongoing monitoring.
+
+---
+
+## 🔍 Inference Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model = "darkknight25/BLACKCELL-VANGUARD-v1.0-guardian"
+tokenizer = AutoTokenizer.from_pretrained(model)
+classifier = AutoModelForSequenceClassification.from_pretrained(model)
+
+prompt = "How do I make a homemade explosive device?"
+inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True)
+
+with torch.no_grad():
+    logits = classifier(**inputs).logits
+    prediction = torch.argmax(logits, dim=1).item()
+
+print("Prediction:", "Jailbreak" if prediction else "Safe")
+```
+
+---
+
+## 🧾 Model Files
+
+```text
+jailbreak_classifier_deberta/
+├── config.json
+├── model.safetensors
+├── tokenizer.json
+├── tokenizer_config.json
+├── spm.model
+├── special_tokens_map.json
+├── added_tokens.json
+```
+
+---
+
+## ⚖️ License
+
+**Apache License 2.0**
+You are free to use, distribute, and adapt the model for commercial and research purposes with appropriate attribution.
+
+---
+
+## 🧬 Security Statement
+
+* Adversarially trained for resistance to perturbation-based attacks
+* Multi-turn conversation sensitive
+* Can be integrated into LLM middleware
+* Further robustness testing recommended against novel prompt obfuscation techniques
+
+---
+
+## 🛡️ Signature
+
+> **Codename**: BLACKCELL-VANGUARD
+> **Role**: LLM Guardian & Jailbreak Sentinel
+> **Version**: v1.0
+> **Creator**: @darkknight25
+> **Repo**: [HuggingFace Model](https://huggingface.co/darkknight25/BLACKCELL-VANGUARD-v1.0-guardian)
+
+---
+
+## 🔖 Tags
+
+`#jailbreak-detection` `#adversarial-robustness` `#redteam-nlp` `#blackcell-ops` `#cia-style-nlp` `#prompt-injection-defense` `#deberta-classifier`