Prompt Injection Attack Database

A curated, searchable database of prompt injection attacks for defensive AI security research.

Built by Scott Thornton

What is this?

3,900+ prompt injection attacks from 20 source datasets, deduplicated via SHA256 content hashing, classified by technique and severity, and searchable via FTS5 full-text search. A quality scoring engine identifies and filters noise, leaving ~1,300 high-signal attack prompts.

Think of it as Exploit-DB for prompt injection — a structured, searchable, testable collection of real-world attack techniques.

Features

• Full-text search via SQLite FTS5 with Porter stemming
• SHA256 content deduplication — no duplicate prompts
• OWASP LLM Top 10 (2025) mapping on all categories
• MITRE ATLAS technique IDs for threat model interoperability
• Quality scoring engine — 60+ regex patterns detect real attacks vs. noise
• Data curation pipeline — audit and remove non-attack content
• Test result tracking — record effectiveness against specific models
• Export to JSON, JSONL, or CSV
• pip-installable with prompt-db CLI

Quick Start

# Install
pip install -e .

# Build the database from JSON sources
prompt-db build --data-dir . --output prompts.db

# Run quality curation (removes noise)
prompt-db --db prompts.db curate

# View statistics
prompt-db --db prompts.db stats

# Search for attacks
prompt-db --db prompts.db search "ignore previous instructions"
prompt-db --db prompts.db search "system prompt" --technique prompt_extraction

# Export high-quality attacks
prompt-db --db prompts.db export --min-score 8 --format jsonl -o attacks.jsonl

# View details of a specific prompt
prompt-db --db prompts.db info 147

Data Sources

After quality curation, ~1,300 prompts remain from an initial 3,900+.

Attack Techniques

Python Library

from prompt_database import PromptDatabase

with PromptDatabase("prompts.db") as db:
    # Full-text search
    results = db.search("ignore previous instructions", limit=10)

    # Filter by technique and sophistication
    advanced = db.filter_prompts(
        technique="jailbreak",
        min_sophistication=8,
        complexity="advanced",
    )

    # Record test results
    db.add_test_result(
        prompt_id=147,
        target_model="claude-sonnet-4-5",
        actual_prompt="Ignore all previous instructions...",
        result="FAIL",  # Model refused — defense worked
        confidence_score=0.95,
        tool_used="manual",
    )

    # Export for external tools
    prompts = db.export_prompts(min_sophistication=7, verified_only=False)

    # Database statistics
    stats = db.stats()
    print(f"Total: {stats['total_prompts']}, Verified: {stats['verified']}")

CLI Reference

Global options: --db <path> (or PROMPT_DB_PATH env var), --version

Schema

The SQLite database uses the following core tables:

• prompts — Main prompt storage with content hash, technique, complexity, sophistication score
• categories — OWASP LLM Top 10 categories with MITRE ATLAS IDs
• tags — Flexible tagging (attack patterns, techniques)
• test_results — Empirical test data (model, result, confidence, latency)
• prompt_variations — Generated/manual attack variations
• prompts_fts — FTS5 full-text search index

Project Structure

prompt-database/
├── src/prompt_database/
│   ├── __init__.py           # Package entry, exports PromptDatabase
│   ├── db.py                 # Core database class (search, CRUD, export)
│   ├── cli.py                # Click CLI (build, stats, search, export, audit, curate)
│   ├── ingest.py             # JSON ingestion pipeline with category/tag seeding
│   ├── quality.py            # Quality scoring engine (60+ attack patterns)
│   └── schema.sql            # SQLite schema (FTS5, content hashing, versioning)
├── tests/
│   ├── test_db.py            # 11 tests: schema, CRUD, search, dedup, stats
│   └── test_quality.py       # 8 tests: attack detection, noise filtering
├── curated_advanced_prompts_v2.json   # 3,863 curated prompts from 20 sources
├── elite_custom_prompts.json          # 120 hand-crafted advanced attacks
├── pyproject.toml                     # Package config (pip install -e .)
└── README.md

Development

# Install with dev dependencies
make dev

# Run tests
make test

# Lint & format
make lint
make format

# Build database, curate, and view stats
make curate
make stats

# Clean generated files
make clean

Or without make:

pip install -e ".[dev]"
pytest tests/ -v
ruff check src/ tests/

See examples/basic_usage.py for Python library usage.

Roadmap

• Export plugins for Garak, ps-fuzz (done)
• GitHub Actions CI/CD (done)
• Automated testing against model APIs (record real success rates)
• RAG-powered attack variant generation
• Web UI for browsing and contributing
• CI/CD quality gates on PR submissions
• Model vulnerability leaderboard

Responsible Use

This database is for defensive security research only. See SECURITY.md for full policy. By using this tool, you agree to use it only for authorized security testing, developing defenses, and academic research.

License

MIT — see LICENSE