At fischer³, we are developing cutting-edge open-source security solutions that leverage the power of artificial intelligence. Our mission is to strengthen the cybersecurity ecosystem by creating and maintaining robust, accessible tools that help organizations protect their assets and infrastructure.

Zero Trust AI Learning Project and Open-source software

Since Oct 2025, this open learning project provides a structured path for developers to understand:

Model Context Protocol (MCP) - connecting AI agents to tools and resources


Agent2Agent Protocol (A2A) - enabling multi-agent communication and orchestration


Security Concerns - identifying vulnerabilities in protocol implementations


Secure Implementation - building production-ready systems with proper security controls

 

What makes this different?

 

  • Shows vulnerable code first — learn to recognize security anti-patterns

  • Explains the risks — understand why vulnerabilities matter

  • Demonstrates fixes — implement proper security controls

  • Provides context — in-depth articles explain complex concepts
  • Multiple learning paths — three complete example progressions across domains

 

more...

 

GitHub project: learn-a2a-security.fischer3.net

Zero Trust AI Learning Project and Open-source software

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Zero-Trust AI Framework — Never trust. Always verify. Build secure AI.

 

License Status: Early Development — Stage 0 (Foundation)

 

Mission: democratize AI security with an open, educational framework enabling developers to build, evaluate, and secure specialized AI agents using zero-trust principles.

 

Why this matters: as AI systems evolve into interconnected, autonomous agents, the attack surface expands and traditional perimeter models fail. We need zero-trust architecture designed for AI agents.

 

The problem:

  • Agents communicate via protocols like MCP
  • Multi-agent systems create complex trust boundaries
  • A compromised agent can affect entire networks
  • Few security frameworks target agentic architectures
  • Existing zero-trust models weren’t designed for AI agents

 

Core principles:

  • Verify every agent interaction (no implicit trust)
  • Assume compromise (design for resilience)
  • Least-privilege access (minimal permissions)
  • Continuous monitoring (real-time evaluation)
  • Context-aware security (dynamic policies)

 

Educational & staged approach:

Stage 0: Foundation — threat modeling and architecture (current)

Stage 1: Guardian Core — basic detection and monitoring

Stage 2: MCP Security — protocol analysis and verification

Stage 3: RAG Integration — dynamic security policies

Stage 4: Multi-Agent Security — behavior profiling and anomaly detection

Stage 5: Production Hardening — enterprise-ready deployment

 

What we’re building:

 

  • Guardian Model — analyzes agent-to-agent communications, detects prompt injection, data exfiltration, and privilege escalation, enforces policies in real time, and provides explainable decisions.
  • Reusable Templates — modular patterns for securing specialized models, RAG with evolving policies, and safe agent collaboration with continuous verification.
  • Educational Resources — clear docs, hands-on examples, and working code that bridge security expertise and AI development.

 

See ROADMAP.md for detailed stage breakdowns.

 

Domain: zero-trust.ai