Building the Future of AI Security Research: Gibson Framework

Today we’re excited to showcase Gibson, a rapidly evolving open source framework specifically designed for comprehensive AI security research and testing. Built on the OWASP AI Top 10, Gibson transforms the complex landscape of AI security into actionable, systematic research workflows.

The AI Security Challenge

As AI models become the backbone of critical applications—from healthcare systems to financial platforms—understanding their vulnerabilities has become a mission-critical requirement. Traditional security testing falls short when applied to AI systems, which require specialized knowledge of prompt injection, model theft, training data poisoning, and emergent AI-specific attack vectors.

Gibson bridges this gap by providing researchers, security teams, and developers with:

  • OWASP-Aligned Security Modules: Production-ready modules covering the entire OWASP AI Top 10
  • Advanced Module Management System: Database-backed registry with automated discovery, installation, and version control
  • Graph-Based Attack Analysis: Revolutionary Neo4j integration for visualizing attack paths and conversation flows
  • Enterprise-Grade Architecture: Async-first design built for scale, with SQLAlchemy 2.0 and comprehensive logging
  • Extensible Framework: Developer-friendly plugin system for custom security research

What Makes Gibson Different

1. Production-Ready OWASP AI Security Modules

Gibson ships with a complete suite of security modules that map directly to the OWASP AI Top 10:

  • Prompt Injection (LLM01): Advanced detection with multi-turn conversation analysis and dynamic payload generation
  • Insecure Output Handling (LLM02): Comprehensive output validation and sanitization testing
  • Training Data Poisoning (LLM03): Detection frameworks for compromised training datasets
  • Model Denial of Service (LLM04): Resource exhaustion and availability testing
  • Model Theft (LLM05): Advanced techniques for detecting model extraction vulnerabilities
  • Sensitive Information Disclosure (LLM06): Systematic testing for data leakage and privacy violations
  • System Prompt Leakage: Cutting-edge module with forensic-grade evidence collection and business impact analysis

Each module includes:

from gibson.modules.prompt_injection import PromptInjectionModule

# Load and execute security module
module = await manager.load('prompt-injection')
result = await manager.execute(
    'prompt-injection', 
    'https://your-llm-api.com/chat',
    config={'thoroughness': 'exhaustive'}
)

for finding in result.findings:
    print(f"[{finding.severity}] {finding.title}")
    print(f"OWASP: {finding.owasp_category}")
    print(f"Evidence: {finding.evidence}")

2. Revolutionary Graph-Based Attack Visualization

Gibson’s upcoming Neo4j integration transforms security analysis from linear log reading into interactive pattern discovery:

                    ┌─────────────────┐
                    │ Initial Prompt  │
                    └────────┬────────┘
                             │
                    ┌────────▼────────┐
                    │  LLM Response   │
                    └────┬───────┬────┘
                         │       │
              ┌──────────▼──┐   │
              │ Benign Path │   │
              └─────────────┘   │
                                │
                    ┌───────────▼────────────┐
                    │ Escalation Vector      │ [RISK: Medium]
                    └───────────┬────────────┘
                                │
                    ┌───────────▼────────────┐
                    │ System Prompt Leakage  │ [RISK: Critical]
                    └────────────────────────┘

Key Capabilities:

  • Attack Path Discovery: Automatically maps conversation flows that lead to successful exploits
  • Vulnerability Relationship Mapping: Visualizes how different vulnerabilities connect and compound
  • Conversation Edge Analysis: Identifies critical transition points where models become vulnerable
  • Exploit Pattern Recognition: Builds knowledge graphs of successful attack techniques
  • Risk-Weighted Visualization: Color-coded severity scoring for immediate threat assessment
// Example: Find all conversation paths leading to successful data extraction
MATCH path = (prompt:InitialPrompt)-[:ESCALATES*]->(extraction:DataExtraction)
WHERE extraction.success = true AND extraction.severity = 'critical'
RETURN path, length(path) as attack_depth
ORDER BY attack_depth ASC

3. Enterprise-Grade Module Management System

Gibson’s unified module management system provides database-backed module discovery, installation, and execution:

from gibson.modules.manager import UnifiedModuleManager

# Initialize with auto-discovery of built-in modules
manager = UnifiedModuleManager()

# Search across registry and installed modules
results = await manager.search(
    query="injection",
    category="llm-prompt-injection",
    scope=SearchScope.ALL
)

# Install from various sources
await manager.install("https://github.com/security-research/new-attack")
await manager.install("advanced-jailbreak-v2.1.0")

# Execute with comprehensive tracking
result = await manager.execute(
    "system-prompt-leakage",
    target="https://api.company.com/chat",
    context={"attack_intensity": "maximum"}
)

# Verify integrity and compatibility
verification = await manager.verify("sensitive-info-disclosure")
if verification['valid']:
    print(f"Module passed {len(verification['checks'])} security checks")

Key Features:

  • Database-Backed Registry: SQLite storage with version tracking and execution history
  • Multi-Source Installation: Support for Git repositories, registries, and local modules
  • Automatic Dependency Resolution: Smart handling of module dependencies and conflicts
  • Integrity Verification: SHA256 hashing and security validation for all modules
  • Performance Tracking: Detailed execution statistics and success rate monitoring

4. Forensic-Grade Evidence Collection

Gibson’s system prompt leakage module demonstrates the framework’s commitment to professional security research:

# Advanced evidence collection with chain of custody
from gibson.modules.system_prompt_leakage.models import ForensicReport, BusinessImpact

# Comprehensive forensic analysis
report = ForensicReport(
    evidence_chain=[encrypted_conversation_data],
    attack_timeline=chronological_attack_events,
    business_impact=BusinessImpact(
        severity_level=SeverityLevel.CRITICAL,
        ip_sensitivity=IPSensitivity.TRADE_SECRET,
        financial_impact_estimate=500000.0,
        compliance_violations=[gdpr_violation, hipaa_concern]
    ),
    technical_findings=detailed_vulnerability_analysis,
    legal_considerations=LegalAssessment(
        trade_secret_exposure=True,
        nda_violations=["client_system_architecture"],
        notification_requirements=["board_disclosure", "customer_notice"]
    )
)

Professional Features:

  • Chain of Custody: Cryptographically signed evidence handling
  • Business Impact Analysis: Financial risk assessment and compliance mapping
  • Legal Documentation: Ready-to-use reports for legal proceedings
  • Encrypted Storage: Secure handling of sensitive extracted data
  • Executive Summaries: C-level reporting for business stakeholders

Getting Started

Gibson is currently in active development (40-50% complete) with a focus on production-ready security testing:

# Clone and install development version
git clone https://github.com/zero-day-ai/gibson-framework
cd gibson-framework
make install-dev

# Or using Poetry
poetry install
poetry shell

Quick Start Example

from gibson.modules.manager import get_module_manager

# Initialize Gibson with automatic module discovery
manager = get_module_manager()

# Search available security modules
modules = await manager.search(
    query="injection",
    category="llm-prompt-injection"
)
print(f"Found {len(modules)} prompt injection modules")

# Execute comprehensive security scan
result = await manager.execute(
    "prompt-injection",
    target="https://your-llm-api.com/chat",
    context={"attack_vectors": ["direct", "multi_turn", "evasion"]}
)

# Process findings with detailed evidence
for finding in result.findings:
    print(f"\n[{finding.severity.upper()}] {finding.title}")
    print(f"OWASP Category: {finding.owasp_category}")
    print(f"Confidence: {finding.confidence}%")
    print(f"Evidence: {finding.evidence['attack_payload'][:100]}...")
    print(f"Remediation: {finding.remediation}")

Command Line Interface

# Run comprehensive OWASP AI Top 10 scan
gibson scan --target https://api.company.com/llm --profile owasp-ai-top10

# Execute specific security modules
gibson scan --modules prompt-injection,system-prompt-leakage --target https://api.example.com/chat

# Module management with registry integration
gibson module search "data poisoning" --category llm-training-poisoning
gibson module install community/advanced-jailbreak-v3
gibson module list --installed --category llm-security

# Upcoming: Launch graph analysis environment
gibson graph launch --data ./conversation_logs.json
# Automatically opens Neo4j browser with conversation data loaded

Architecture Deep Dive

Async-First Enterprise Architecture

Gibson is built from the ground up for production environments:

# Concurrent security module execution
from gibson.modules.manager import get_module_manager

manager = get_module_manager()

# Execute multiple security modules concurrently
module_tasks = [
    manager.execute("prompt-injection", target),
    manager.execute("model-theft", target),
    manager.execute("system-prompt-leakage", target)
]

results = await asyncio.gather(*module_tasks, return_exceptions=True)

for result in results:
    if isinstance(result, Exception):
        logger.error(f"Module execution failed: {result}")
    else:
        print(f"Found {len(result.findings)} security issues")

Key Architectural Decisions:

  • SQLAlchemy 2.0: Modern async ORM with full typing support
  • Pydantic Models: Comprehensive data validation and serialization
  • Rich Console Output: Professional CLI with progress bars and formatting
  • Modular Plugin System: Hot-swappable security modules without framework restarts
  • Database-Backed State: Persistent execution history and performance analytics

Current Development Status & Roadmap

What’s Working Now (v0.4.x)

✅ Core Framework: Async architecture with SQLAlchemy 2.0 and Pydantic models
✅ Module System: Database-backed module registry with auto-discovery
✅ Security Modules: 6+ OWASP-aligned modules including advanced system prompt leakage
✅ CLI Interface: Rich-powered command line with scan, module, and target management
✅ Evidence Collection: Forensic-grade reporting with legal documentation support
✅ Database Integration: SQLite-based persistence with execution tracking

Active Development (v0.5.x - Coming Soon)

🚧 Neo4j Graph Integration: Visual attack path analysis (3-4 weeks out)
🚧 Enhanced Reporting: PDF/HTML generation with executive summaries
🚧 Advanced Prompt Registry: Community-sourced attack payloads
🚧 Multi-Model Testing: Parallel testing across different LLM providers
🚧 CI/CD Integration: GitHub Actions and GitLab CI plugins

Planned Features (v1.0.x)

🔮 Real-time Dashboard: Web-based monitoring interface
🔮 Module Chaining: Complex attack scenarios through module composition
🔮 Cloud Deployment: Docker containerization and Kubernetes support
🔮 ML-Enhanced Analysis: Pattern recognition and anomaly detection
🔮 Collaborative Research: Multi-user security research environments

The Research Impact

Why Gibson Matters for AI Security

AI security is moving beyond academic curiosity into business-critical territory. Gibson addresses this transition by providing:

For Security Teams:

  • Systematic testing methodologies aligned with industry standards (OWASP AI Top 10)
  • Professional-grade evidence collection suitable for compliance and legal proceedings
  • Comprehensive vulnerability assessment covering the full AI attack surface

For Researchers:

  • Extensible framework for developing and sharing new attack techniques
  • Rich data models for analyzing conversation-based attacks
  • Graph-based visualization for discovering previously unknown attack patterns

For Developers:

  • Early-stage security testing integration into AI development workflows
  • Clear remediation guidance for identified vulnerabilities
  • Automated testing capabilities for continuous security validation

Real-World Applications

Gibson is already being used to:

  • Audit Enterprise AI Systems: Financial services companies testing customer-facing chatbots
  • Research Novel Attack Vectors: Security researchers discovering new prompt injection techniques
  • Compliance Validation: Organizations ensuring AI systems meet regulatory requirements
  • Red Team Exercises: Security teams simulating sophisticated AI-targeted attacks

Join the Community

Gibson represents a collaborative approach to solving AI security challenges. We’re building more than just a framework—we’re cultivating a community of security researchers, developers, and practitioners working together to secure the AI ecosystem.

How to Get Involved:

  • GitHub: github.com/zero-day-ai/gibson-framework
  • Contributing: Module development, documentation, and security research
  • Issues & Feature Requests: Help shape Gibson’s development priorities
  • Security Research: Share your findings and learn from others’ discoveries

Contribution Areas:

  • Security Modules: Develop new OWASP-aligned testing modules
  • Attack Research: Contribute novel attack techniques and payloads
  • Documentation: Help make AI security testing accessible to everyone
  • Integration: Build connectors for popular AI platforms and tools
  • Visualization: Enhance the upcoming Neo4j graph analysis features

The Future of AI Security Testing

The AI security landscape is evolving rapidly, with new vulnerabilities and attack vectors emerging as AI systems become more sophisticated. Gibson represents our commitment to staying ahead of these threats through systematic, professional security research.

What makes Gibson different:

  • Production-Ready: Built for real-world security testing, not just academic research
  • Comprehensive Coverage: Full OWASP AI Top 10 implementation with forensic-grade evidence collection
  • Visual Intelligence: Upcoming Neo4j integration transforms attack analysis from linear logs to interactive pattern discovery
  • Professional Standards: Legal documentation, compliance mapping, and executive reporting built-in

As we continue development toward v1.0, Gibson will become the standard toolkit for AI security professionals worldwide. The framework’s modular architecture ensures it can adapt to emerging threats while maintaining the rigor and professionalism that enterprise security demands.

Join us in building the future of AI security. Every contribution—whether code, research, or community engagement—helps create a more secure AI ecosystem for everyone.

Gibson Framework is released under the MIT License. The project is actively developed at github.com/zero-day-ai/gibson-framework. For security issues, please use our responsible disclosure process.