README.md

metadata

language:
  - en
license: mit
task_categories:
  - text-generation
  - text-classification
tags:
  - cyber-security
  - red-teaming
  - chain-of-thought
  - agent-reasoning
  - synthetic
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 `

` block analyzing the anomaly, evidence, and conclusion.
3. **Verdict**: Structured labels for `STATUS`, `SEVERITY`, and `NEXT_TEST`.

## Attack Categories
The dataset covers a wide spectrum of modern web threats:
- **Injection**: SQLi, XSS (Reflected/Stored), Command Injection, LDAPi.
- **Business Logic**: Price Manipulation, Mass Assignment, Race Conditions.
- **Protocol**: HTTP Request Smuggling, Host Header Injection.
- **Anomalies**: Zero-Day simulations (Unknown patterns) and Fuzzing noise.
- **Benign**: High-entropy legitimate traffic to train False Positive rejection.

## Sample Structure
```json
{
"instruction": "Analyze: GET /api/v1/user?id=1' OR 1=1 HTTP/1.1...",
"response": "\nObservation: ...\nHypothesis: ...\nEvidence: ...\nDecision: ...\n

\nSTATUS: VULNERABLE...", "hypothesis": "SQL Injection", "confirmed": true, "severity": "Critical" }