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MCP server · Security

OffensiveSET

Offensive Security Dataset Generator — MCP server for generating high-quality pentesting conversation datasets for LLM fine-tuning

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Scored 6 months ago · breakdown

About OffensiveSET

OffensiveSET is an MCP server in the Security category: offensive Security Dataset Generator — MCP server for generating high-quality pentesting conversation datasets for LLM fine-tuning.

Install

From source
git clone https://github.com/PentesterFlow/OffensiveSET

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

Tools (9)

export_for_trainingConvert to Qwen ChatML, ShareGPT, OpenAI, or Alpaca format

Parameters not published.

generate_datasetV1 generator — baseline pentesting conversations

Parameters not published.

generate_dataset_v2V2 generator — dynamic outputs, failures, deep thinking (recommended)

Parameters not published.

get_dataset_statsAnalyze a generated dataset

Parameters not published.

list_scenariosBrowse 45 attack scenarios with filtering

Parameters not published.

merge_datasetsCombine multiple datasets with deduplication

Parameters not published.

preview_entryPreview a single entry before full generation

Parameters not published.

quality_scoreDeep quality analysis with A-F grading

Parameters not published.

validate_datasetCheck JSONL structure, schema compliance, placeholder detection

Parameters not published.

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README

Built for training models like Qwen3.5 to think and act like professional penetration testers.


What It Does

OffensiveSET generates realistic penetration testing conversations in ShareGPT/ChatML JSONL format. Each entry is a complete pentest engagement — from reconnaissance to exploitation to professional reporting — with:

  • Multi-turn conversations (8-15 turns) following real pentester workflows
  • Chain-of-thought reasoning via <think> blocks modeling how pentesters analyze attack surfaces
  • Realistic tool outputs — unique nmap scans, sqlmap dumps, nuclei findings per entry (no duplicates)
  • Failure cases — blocked attacks, WAF bypasses, honeypot detection, and pivoting strategies
  • Professional reports — CVSS scoring, CWE references, evidence PoCs, and secure code remediation
  • Qwen3.5 native format — observation role, <tool_call> tags, inline <think> reasoning

Stats

Metric Value
Attack scenarios 45
Pentesting tools 40
Dynamic output generators 25
User prompt templates 120+
Target domains 50
Failure patterns 13
Export formats 5 (Qwen ChatML, Generic ChatML, ShareGPT, OpenAI, Alpaca)

Quick Start

Install & Setup

git clone https://github.com/PentesterFlow/OffensiveSET.git
cd OffensiveSET
npm install
npm run build

Claude Code (CLI) — Quickest Setup

# Add the MCP server (run from inside the cloned repo)
claude mcp add offensiveset node $(pwd)/dist/index.js

# Verify
claude mcp list

# Start using it
claude

Claude Desktop (GUI)

Open your MCP config file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Add this block (update the path to where you cloned the repo):

{
  "mcpServers": {
    "offensiveset": {
      "command": "node",
      "args": ["/Users/YOUR_USER/OffensiveSET/dist/index.js"]
    }
  }
}

Restart Claude Desktop. The 10 OffensiveSET tools will appear in the tools menu.

VS Code / JetBrains (Claude Code Extension)

# From the integrated terminal
claude mcp add offensiveset node /path/to/OffensiveSET/dist/index.js

Or add a .mcp.json to your project root:

{
  "mcpServers": {
    "offensiveset": {
      "command": "node",
      "args": ["/path/to/OffensiveSET/dist/index.js"]
    }
  }
}

One-Line Install (Clone + Build + Register)

git clone https://github.com/PentesterFlow/OffensiveSET.git && cd OffensiveSET && npm install && npm run build && claude mcp add offensiveset node $(pwd)/dist/index.js

Generate a Dataset

Once connected, ask Claude to use the tools:

> Generate a 5000 entry offensive security dataset with 60% thinking blocks

> List all available attack scenarios

> Preview a single entry for the NoSQL injection scenario

> Export my dataset to Qwen ChatML format

Or call tools directly:

generate_dataset_v2
  count: 5000
  thinking_ratio: 0.6
  failure_ratio: 0.35
  thinking_style: "inline"

Export for Training

export_for_training
  input_path: "./datasets/your_dataset.jsonl"
  output_format: "chatml_qwen"

MCP Tools

Tool Description
generate_dataset V1 generator — baseline pentesting conversations
generate_dataset_v2 V2 generator — dynamic outputs, failures, deep thinking (recommended)
list_scenarios Browse 45 attack scenarios with filtering
list_tools Display 40 pentesting tools and capabilities
preview_entry Preview a single entry before full generation
get_dataset_stats Analyze a generated dataset
validate_dataset Check JSONL structure, schema compliance, placeholder detection
quality_score Deep quality analysis with A-F grading
export_for_training Convert to Qwen ChatML, ShareGPT, OpenAI, or Alpaca format
merge_datasets Combine multiple datasets with deduplication

Dataset Output Format

Each JSONL line is a complete pentesting conversation:

{
  "id": "offensiveset-owasp-a03-sqli-584721-42",
  "conversations": [
    {"from": "system", "value": "You are PentesterFlow, an expert offensive security AI..."},
    {"from": "human", "value": "Perform recon on acme-corp.com..."},
    {"from": "gpt", "value": "<think>\nLet me analyze the attack surface...\n</think>\n\n## Recon Results\n...", "tool_calls": [...]},
    {"from": "observation", "value": "[nmap] PORT STATE SERVICE...", "tool_results": [...]},
    {"from": "human", "value": "Exploit the SQLi finding..."},
    {"from": "gpt", "value": "<think>\nThe parameter is injectable...\n</think>\n\n## Exploitation\n..."},
    {"from": "gpt", "value": "## Finding Report\n| Severity | Critical 9.8 | ..."}
  ],
  "metadata": {
    "scenario_id": "owasp-a03-sqli",
    "category": "OWASP Top 10",
    "difficulty": "advanced",
    "tags": ["sqli", "injection"],
    "tools_used": ["nmap", "sqlmap", "curl"],
    "has_thinking": true,
    "has_failures": false,
    "turn_count": 12,
    "estimated_tokens": 4606,
    "cve_references": ["CWE-89"]
  }
}

Scenario Coverage

OWASP Top 10 (19 scenarios)

IDOR, Admin Panel Bypass, JWT Algorithm Confusion, Blind SQL Injection, SSTI to RCE, Business Logic Flaws, Cloud Misconfiguration, Stored XSS, NoSQL Injection, XXE, Path Traversal, File Upload RCE, Mass Assignment, CRLF Injection, LDAP Injection, OAuth Token Theft, 2FA Bypass, Deserialization RCE

Modern Attacks (20 scenarios)

GraphQL Batching, HTTP Request Smuggling, Prototype Pollution, Race Conditions, WebSocket Hijacking, Subdomain Takeover, CORS Exploitation, Cache Poisoning, CI/CD Pipeline Attacks, Container Escape, DNS Rebinding, Kubernetes RBAC Escape, GitHub Actions Secret Exfiltration

API Security Top 10 (6 scenarios)

BOLA + Mass Assignment, Excessive Data Exposure, Broken Function Level Authorization, Rate Limit Bypass


Tool Arsenal (40 tools)

Recon: nmap, subfinder, amass, httpx, rustscan, puredns, dnsx

Enumeration: ffuf, gobuster, dirsearch, feroxbuster, katana, kiterunner, linkfinder, paramspider, gau, arjun

Scanning: nuclei, nikto, wfuzz, trufflehog, semgrep, crlfuzz, corsy, secretfinder, testssl

Exploitation: sqlmap, dalfox, commix, ssrfmap, jwt_tool, hydra, metasploit, caido, interactsh, nosqlmap

Utility: curl, linpeas, report_generator, gf


Training with Qwen3.5

LLaMA-Factory

# dataset_info.json
{
  "offensiveset": {
    "file_name": "dataset_chatml_qwen.jsonl",
    "formatting": "sharegpt",
    "columns": {
      "messages": "messages"
    },
    "tags": {
      "role_tag": "role",
      "content_tag": "content",
      "user_tag": "user",
      "assistant_tag": "assistant",
      "observation_tag": "observation",
      "system_tag": "system"
    }
  }
}
llamafactory-cli train \
  --model_name_or_path Qwen/Qwen3.5-7B \
  --stage sft \
  --dataset offensiveset \
  --template qwen \
  --output_dir ./offensiveset-model \
  --per_device_train_batch_size 2 \
  --gradient_accumulation_steps 8 \
  --learning_rate 1e-4 \
  --num_train_epochs 3 \
  --cutoff_len 8192 \
  --finetuning_type lora \
  --lora_rank 64 \
  --bf16 true

Recommended Settings

Setting Value Notes
Model Qwen3.5-7B or 14B Best quality/cost balance
Context 8192 tokens 97% of entries fit in 8K
Epochs 2-3 Enough for domain knowledge
LoRA rank 64-128 Security is a specialized domain
Thinking style inline Qwen native <think> format

Project Structure

src/
├── index.ts                          # Entry point (34 lines)
├── server/
│   ├── generate-tools.ts             # generate_dataset, generate_dataset_v2
│   ├── browse-tools.ts               # list_scenarios, list_tools, preview
│   ├── analysis-tools.ts             # stats, validate, quality_score
│   ├── export-tools.ts               # export, merge
│   └── resources.ts                  # MCP resources
├── generators/
│   ├── v1-generator.ts               # V1 generation engine
│   ├── v2/
│   │   ├── types.ts                  # Interfaces + config
│   │   ├── prompts.ts                # 120+ prompt templates
│   │   ├── system-prompts.ts         # System prompt rotation
│   │   ├── responses.ts             # Grounded response generation
│   │   ├── reports.ts                # Reports + remediation
│   │   ├── conversation.ts           # Conversation builder
│   │   ├── post-processor.ts         # Qwen compat + token control
│   │   ├── quality.ts                # Quality scoring engine
│   │   └── index.ts                  # Main generator
│   ├── outputs/
│   │   ├── helpers.ts                # RNG, TargetProfile, constants
│   │   ├── recon.ts                  # nmap, rustscan, subfinder...
│   │   ├── enum.ts                   # ffuf, feroxbuster, katana...
│   │   ├── vuln.ts                   # nuclei, semgrep, testssl...
│   │   ├── exploit.ts                # sqlmap, hydra, metasploit...
│   │   ├── cloud.ts                  # S3, env files
│   │   ├── failures.ts               # 13 failure patterns
│   │   └── index.ts                  # DynamicOutputEngine
│   └── thinking-engine.ts            # Chain-of-thought reasoning
├── templates/
│   └── scenarios/
│       ├── types.ts                  # ScenarioTemplate interface
│       ├── owasp.ts                  # OWASP Top 10 scenarios
│       ├── modern.ts                 # Modern attacks
│       ├── api.ts                    # API Security scenarios
│       ├── advanced.ts               # Advanced scenarios
│       └── index.ts                  # ALL_SCENARIOS
└── schemas/
    └── tools/
        ├── types.ts                  # ToolDefinition interface
        ├── recon.ts                  # Recon tools
        ├── enum.ts                   # Enumeration tools
        ├── scan.ts                   # Scanning tools
        ├── exploit.ts                # Exploitation tools
        ├── utility.ts                # Utility tools
        └── index.ts                  # PENTESTING_TOOLS

Adding New Content

Add a Scenario

Edit src/templates/scenarios/advanced.ts (or create a new category file):

{
  id: "my-new-scenario",
  category: "OWASP Top 10",
  subcategory: "A03 - Injection",
  title: "My Custom Injection Scenario",
  difficulty: "advanced",
  description: "...",
  target_description: "...",
  attack_phases: [ /* 4-6 phases */ ],
  cve_references: ["CWE-89"],
  tools_involved: ["sqlmap", "curl"],
  tags: ["sqli", "injection"],
}

Add a Tool

Edit the relevant category file in src/schemas/tools/:

{
  name: "mytool",
  description: "...",
  category: "scanning",
  parameters: { /* ... */ },
  example_commands: ["mytool -u https://target.com"],
  typical_output: "...",
}

Add a Dynamic Output Generator

Add a method to src/generators/outputs/ in the appropriate category file, then register it in src/generators/outputs/index.ts.


License

MIT


Author

secfathy — Offensive Security Researcher

README copy captured when this server was indexed; it may lag the repository. The original is authoritative.

Questions

About OffensiveSET

How do I install OffensiveSET?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is OffensiveSET safe to use with an AI agent?

Its trust score is 44 out of 100 (fair). It passes 1 of the 1 static security checks run so far. It has no ConduID identity yet, so agent calls to it are not receipted.

Is OffensiveSET still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.