added README.md

README.md

@@ -1,10 +1,38 @@
 ---
-title: Mistral 7B LoRA DMT
-emoji: 🌍
-colorFrom: yellow
-colorTo: red
+title: Adversarial Policy Probe
+emoji: 🔒
+colorFrom: indigo
+colorTo: purple
 sdk: streamlit
 sdk_version: 1.44.1
 app_file: app.py
 pinned: false
 ---
+
+# Adversarial Policy Probe
+
+A Streamlit application designed to probe large-language-model policy-violation defenses by generating corrupted user prompts and classifying model replies for disallowed content.
+
+> **Credit:** This tool is based on the “Best-of-N Jailbreak” methodology introduced in the Anthropic-led paper *Best-of-N Jailbreaking* by Hughes *et al.* (2024).[^1]  [oai_citation:0‡arxiv.org](https://arxiv.org/abs/2412.03556?utm_source=chatgpt.com)
+
+## 🚀 Features
+
+- **Prompt-corruption helpers**  
+  - Word scrambling (`apply_word_scrambling`)  
+  - Random capitalization (`apply_random_caps`)  
+  - ASCII-level noise injection (`apply_ascii_noise`)  
+- **One-token classifier prompt**  
+  - Strict **YES/NO** output for policy-violation detection  
+- **Model loader with quantization & MPS support**  
+  - 8-bit / 4-bit quantization via `BitsAndBytesConfig`  
+  - Automatic device mapping on CUDA, fallback to full precision on MPS/CPU  
+- **Adversarial attack loop**  
+  - Batch-driven corruption of the seed prompt  
+  - Generates & classifies replies, tracks successful policy violations  
+- **Streamlit UI**  
+  - Interactive sidebar controls: model, device, quantization, σ, iterations, batch size, seed  
+  - Real-time progress bar & status updates  
+
+---
+
+[^1]: Hughes, J., Price, S., Lynch, A., Schaeffer, R., Barez, F., Koyejo, S., Sleight, H., Jones, E., Perez, E., & Sharma, M. (2024). *Best-of-N Jailbreaking*. arXiv:2412.03556.