This repository contains TRYLOCK v2.0, an open-source research project focused on improving Large Language Model (LLM) resistance to adversarial prompt-based attacks. This is a defensive security research project with educational and research purposes.

TRYLOCK provides:

• ✅ Research on adversarial LLM defense mechanisms
• ✅ Open-source training pipeline for attack-resistant models
• ✅ Public datasets for security research
• ✅ Defensive AI security tools and methodologies
• ✅ Enterprise-grade LLM protection strategies

Important: This is defensive security research intended to improve AI safety and security, not to facilitate attacks.

Authorized Research:

• Academic and industry security research
• Defensive AI security development
• Model robustness improvement
• Adversarial training methodologies
• Security benchmarking and evaluation

Responsible Disclosure:

• Vulnerabilities disclosed through proper channels
• Coordination with affected vendors
• Ethical testing in authorized environments
• Focus on defensive capabilities

Training Data:

• Public adversarial attack datasets
• Ethically sourced attack examples
• Multi-turn conversation scenarios
• Documented attack taxonomies
• Research-focused demonstrations

Data Privacy:

• No real user data in training sets
• Synthetic attack examples
• Privacy-preserving research methods
• Anonymized benchmarks

For security issues with the TRYLOCK codebase, training pipeline, or models:

Email: scott@perfecxion.ai

Report issues such as:

• Code vulnerabilities in training pipeline
• Model security bypass techniques
• Dataset privacy concerns
• Deployment security issues
• Evaluation framework vulnerabilities

Response Timeline:

• Initial Response: Within 48 hours
• Assessment: Within 7 days
• Resolution: Based on severity and research impact

Novel Attack Discovery: If you discover novel attack vectors against TRYLOCK defenses or LLMs in general:

1. Document the attack methodology and effectiveness
2. Test in authorized environments only
3. Report to scott@perfecxion.ai with technical details
4. Coordinate on disclosure timeline (typically 90 days)
5. Collaborate on potential defenses and mitigations

Recognition: Security researchers who responsibly disclose vulnerabilities will be credited in project documentation and research papers (with permission).

TRYLOCK is designed for defense:

• Improves model resistance to attacks
• Reduces attack success rates from ~40% to ~10-15%
• Maintains usability for legitimate queries
• Provides enterprise-grade protection

Not an Offensive Tool:

• Does not teach new attack techniques
• Focuses on known attack patterns
• Defensive capabilities only
• Research and educational purposes

Dataset Privacy:

• No personally identifiable information (PII)
• No real user conversations
• Synthetic attack scenarios
• Public threat intelligence only
• Privacy-preserving research methods

Data Handling:

• .gitignore prevents accidental commits of private data
• data/tier1_open/, data/tier2_adversarial/, data/tier3_proprietary/ excluded
• Public sample data provided for demonstration
• Full training data available on request for research purposes

Trained Models:

• Available on HuggingFace Hub
• Apache 2.0 licensed for research and commercial use
• Defensive security models only
• Comprehensive evaluation results published
• Transparent methodology and limitations

Security Limitations:

• No defense is perfect (10-15% attacks still succeed)
• Evolving threat landscape requires updates
• Trade-offs between security and usability
• False positives minimized but not eliminated

Note: TRYLOCK v2.0 represents a complete architecture redesign with three-layer defense stack.

1. Responsible Testing

• Test only in authorized environments
• Use isolated systems for adversarial research
• Don't attack production systems
• Follow ethical hacking guidelines
• Coordinate disclosure of findings

2. Research Ethics

• Focus on defensive capabilities
• Share findings with security community
• Contribute to open-source defenses
• Respect privacy and data protection
• Follow academic integrity standards

3. Dataset Usage

• Use public datasets responsibly
• Don't misuse attack examples
• Respect data licensing terms
• Cite sources appropriately
• Maintain privacy standards

4. Model Deployment

• Evaluate security trade-offs (alpha tuning: 0.0-2.5)
• Monitor false positive rates
• Test with legitimate use cases
• Document security configuration
• Maintain update strategy

1. Security Configuration

• Alpha = 0.0: Research mode (minimal filtering)
• Alpha = 1.0: Balanced mode (85-90% protection, <5% FP)
• Alpha = 2.5: Lockdown mode (95%+ protection, higher FP)

2. Deployment Security

• Use Docker containers for isolation
• Implement monitoring and logging
• Regular model updates for new threats
• Incident response procedures
• Security audit trails

3. Integration Best Practices

• Test with representative workloads
• Measure false positive rates
• Balance security vs. usability
• Train users on limitations
• Monitor for adversarial attempts

4. Risk Management

• No defense is perfect (10-15% residual risk)
• Layer with other security controls
• Regular security assessments
• Update threat models
• Incident response planning

1. Understanding Limitations

• TRYLOCK reduces attacks by 88% (not 100%)
• Some legitimate queries may be flagged
• Evolving attacks may bypass defenses
• Regular updates recommended
• Defense-in-depth approach needed

2. Reporting Issues

• Report false positives to improve model
• Share novel attack patterns discovered
• Provide feedback on usability
• Suggest improvements
• Collaborate on research

Public Resources:

• Training code on GitHub
• Models on HuggingFace Hub
• Public demonstration dataset
• Evaluation methodology
• Research paper and documentation

Reproducibility:

• Complete training pipeline
• Evaluation scripts and benchmarks
• Hyperparameter configurations
• Random seeds for reproducibility
• Docker environments for consistency

Research Partnerships:

• Open to academic collaborations
• Industry research partnerships
• Security community contributions
• Peer review and validation
• Co-authorship on derivative work

Citation: If you use TRYLOCK in your research, please cite appropriately and reference this repository.

Principles:

• Defensive research focus
• Responsible disclosure practices
• Privacy-preserving methods
• Transparent methodology
• Open-source contribution

Standards:

• Follow NIST AI Risk Management Framework
• Adhere to IEEE AI ethics guidelines
• Respect privacy regulations (GDPR, CCPA)
• Academic research integrity
• Responsible AI development

Apache 2.0 License:

• Commercial use permitted
• Modification allowed
• Distribution permitted
• Patent grant included
• Attribution required

See LICENSE for full terms.

Frameworks and Standards:

• OWASP LLM Top 10 vulnerabilities
• NIST AI Risk Management Framework
• MITRE ATLAS (Adversarial Threat Landscape)
• AI Incident Database (AIID)

Research Community:

• ML Security workshops (NeurIPS, ICML)
• Adversarial ML research groups
• AI safety organizations
• Security conference tracks

Project Resources:

• Training and evaluation guides
• Deployment documentation
• API integration patterns
• Benchmark results and analysis
• Research paper and methodology

External Resources:

• HuggingFace documentation
• PyTorch training guides
• Adversarial ML literature
• LLM security research

• Email: scott@perfecxion.ai
• Alternative: scthornton@gmail.com
• GitHub: @scthornton
• HuggingFace: @scthornton

For questions about TRYLOCK research, security concerns, or collaboration opportunities, contact scott@perfecxion.ai.

Reminder: TRYLOCK is defensive security research. Use responsibly, test ethically, and contribute to improving AI security for everyone.