Update README.md

README.md

@@ -1,20 +1,60 @@
 ---
-pretty_name: "Security Reasoning Agent - Thought Traces"
-task_categories:
-- text-generation
-- zero-shot-classification
 language:
 - en
+license: mit
+task_categories:
+- text-generation
+- text-classification
 tags:
-- security-reasoning
+- cyber-security
+- red-teaming
 - chain-of-thought
-- pentesting
-- vulnerability-analysis
-- agent
-- cybersecurity
-- burp-suite
-- reasoning
+- agent-reasoning
 - synthetic
-size_categories:
-- n<1K
----
+dataset_info:
+  features:
+  - name: instruction
+    dtype: string
+  - name: response
+    dtype: string
+  - name: hypothesis
+    dtype: string
+  - name: confirmed
+    dtype: bool
+  - name: severity
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 73400320
+    num_examples: 100000
+  download_size: 73400320
+  dataset_size: 73400320
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: security_dataset.jsonl
+---
+
+# 🛡️ Security Analyst CoT Dataset (100k)
+
+A massive-scale, synthetically generated dataset designed to train **AI Security Agents** in offensive reasoning, vulnerability verification, and false positive reduction.
+
+## Dataset Summary
+- **Size:** 100,000 Unique Samples
+- **Format:** JSONL
+- **Focus:** Chain-of-Thought (CoT) Reasoning for Web Security
+- **Logic:** Observation -> Hypothesis -> Evidence -> Decision (O-H-E-D)
+
+## Features
+Each sample simulates a complete cognitive process of a Senior Security Analyst:
+1.  **Instruction**: The raw HTTP request + The server's response code/body.
+2.  **Reasoning**: A `
+
+<div class="think">` block analyzing the anomaly, evidence, and conclusion.<br />3.  **Verdict**: Structured labels for `STATUS`, `SEVERITY`, and `NEXT_TEST`.<br /><br />## Attack Categories<br />The dataset covers a wide spectrum of modern web threats:<br />- **Injection**: SQLi, XSS (Reflected/Stored), Command Injection, LDAPi.<br />- **Business Logic**: Price Manipulation, Mass Assignment, Race Conditions.<br />- **Protocol**: HTTP Request Smuggling, Host Header Injection.<br />- **Anomalies**: Zero-Day simulations (Unknown patterns) and Fuzzing noise.<br />- **Benign**: High-entropy legitimate traffic to train False Positive rejection.<br /><br />## Sample Structure<br />```json<br />{<br />  "instruction": "Analyze: GET /api/v1/user?id=1' OR 1=1 HTTP/1.1...",<br />  "response": "<think>\nObservation: ...\nHypothesis: ...\nEvidence: ...\nDecision: ...\n</div>
+
+\nSTATUS: VULNERABLE...",
+  "hypothesis": "SQL Injection",
+  "confirmed": true,
+  "severity": "Critical"
+}