Security infrastructure for AI agents in production
QYRA Labs builds control layers for MCP servers — protecting AI agents in production from prompt injection, data exfiltration and unauthorized actions.
MCP Security
Security architecture for Model Context Protocol. Protecting the interface between AI agents and external systems.
Control Layers
Deterministic and semantic analysis combined. Enforcing boundaries and predictable behaviour in production.
Audit & Compliance
Full audit trails with integrity verification. Human-in-the-loop for sensitive operations. GDPR-ready architectures.
What we're building
MCP Firewall Beta
Security infrastructure for MCP servers. Detects and blocks prompt injection, jailbreaks, tool hijacking and data exfiltration before they reach your systems.
Built for production environments where AI agents interact with databases, file systems and external APIs.
MCP Security Overview
High-level documentation of security considerations, architecture principles and design intent for MCP and LLM-based systems.
Covers why security for AI agents requires dedicated infrastructure, how deterministic and probabilistic controls coexist, and where common failure modes appear in real-world deployments.
This is conceptual documentation, not a reference implementation.
Topics covered:
- Dual-layer security architecture
- Prompt injection attack vectors
- Tool hijacking prevention
- Human-in-the-loop workflows
- Audit and compliance patterns
Written for engineers evaluating AI security architecture and technical decision-makers planning MCP deployments.
Why AI security requires dedicated infrastructure
Traditional security tools were not designed for probabilistic systems. AI agents introduce failure modes that firewalls and WAFs cannot address.
When an AI agent connects to your database, file system or API through MCP, it operates with real credentials and real access. A successful prompt injection doesn't just return wrong answers — it can exfiltrate data, modify records or trigger actions the user never intended.
The attack surface is fundamentally different. Threats arrive as natural language, embedded in seemingly legitimate requests. They exploit the gap between what an instruction says and what it means. Pattern matching alone cannot solve this.
Jailbreaks, context manipulation, tool hijacking, indirect injection through retrieved content — these are not theoretical risks. They are documented, reproducible and increasingly automated.
QYRA Labs builds infrastructure specifically for this problem space. Not wrappers. Not plugins. Dedicated security layers designed from first principles for the unique characteristics of AI agent communication.
Problem-first
Architecture follows constraints, not trends. Every design decision traces back to a specific threat model and documented attack vector.
Production-tested
Built for real operational environments with real traffic. Validated against a broad range of documented attack patterns and realistic workloads.
Transparent
Full audit trails with integrity verification. Explainable decisions. No black boxes where security is concerned.
Human-in-the-loop
Sensitive operations require explicit approval. Automated detection with human oversight for critical decisions.
Contact
For access to MCP Firewall, research collaboration, or technical inquiries.