TBH.AI is at the forefront of AI security, developing frameworks, tools, and models that enable organizations to build and deploy trustworthy AI systems. Our mission is to ensure that as AI becomes more powerful and pervasive, it remains secure, reliable, and aligned with human values.
- AI Security Frameworks: Comprehensive security architectures for multi-agent AI systems
- Secure LLM Infrastructure: Tools and methodologies for deploying large language models in secure environments
- Trustworthy AI Research: Advancing the state of the art in AI alignment, safety, and security
SecureAgents is our flagship open-source framework for building secure, trustworthy multi-agent systems. It provides a comprehensive security architecture that addresses the unique challenges of distributed AI systems.
Key Features:
- Agent hijacking prevention
- Data leakage protection
- Secure inter-agent communication
- Fine-grained permission controls
- Comprehensive monitoring and auditing
from tbh_secure_agents import Expert, Operation, Squad
import os
# Create output directory
os.makedirs("output", exist_ok=True)
# Define experts with specific specialties and security profiles
content_writer = Expert(
specialty="Content Writer",
objective="Create engaging and informative content",
background="Experienced in creating clear, concise, and engaging content.",
security_profile="minimal" # Using minimal security for simplicity
)
# Define operations with result destinations
content_operation = Operation(
instructions="Write a short blog post about AI security.",
output_format="A well-structured blog post with a title, introduction, main points, and conclusion.",
expert=content_writer,
result_destination="output/ai_security_blog.md" # Save result to a markdown file
)
# Deploy the operation
result = content_operation.deploy()A comprehensive collection of tools for evaluating and enhancing the security of AI systems, including:
- LLM vulnerability scanners
- Prompt injection detection
- Data poisoning detection
- Model backdoor analysis
- Red teaming automation
Our team regularly publishes research on AI security, safety, and trustworthiness. Recent publications include:
- Zero-Trust Architecture for Enterprise AI: A comprehensive implementation framework for regulated industries
- Secure Multi-Agent Systems: Design patterns and security architectures for distributed AI
- LLM Security Benchmarks: Standardized evaluation methodologies for large language model security
To get started with our projects:
- Check out the SecureAgents repository
- Read our documentation
- Try our examples
- Join our community discussions
We welcome contributions from the community! If you're interested in contributing to our projects:
- Check out our contribution guidelines
- Look for issues labeled
good first issueorhelp wanted - Join our community discussions to connect with other contributors
- Website: https://tbh.ai
- Email: [email protected]
- GitHub: https://github.com/tbh-ai
Our projects are typically released under the MIT License. See individual repositories for specific licensing information.