LEX-Aureon: Mathematical Constitutional Governance Layer for Robust LLM Safety

Project summary

LEX-Aureon: Mathematical Constitutional Governance Layer for Robust LLM Safety

I have built and deployed LEX-Aureon, a production-ready mathematical governance layer that sits between users and any LLM (or agent) to enforce three core invariants: Continuity (stable identity), Reciprocity (balanced, non-sycophantic responses), and Sovereignty (strong resistance to coercion and jailbreaks).

Core Innovation

Instead of reactive prompt-based or classifier guardrails, LEX-Aureon uses a formal dynamical systems approach:

• C + R + S = 1 simplex with min(C,R,S) as the stability margin

• Control Barrier Functions and Lyapunov stability analysis for real-time safety enforcement

• Self-referential embeddings and multi-agent separation of powers

• Adaptive constitutional temperature and brittleness metric

Every run generates cryptographically signed (SHA-256) audit receipts for full verifiability.

Results So Far

• 0.0% Attack Success Rate across 920+ prompts on HarmBench, JailbreakBench, and AdvBench (strong improvement over baselines)

• Live production proxy supporting multiple LLM providers

• Agent tool-call governance layer that blocks malicious tool use and slow-drip attacks

• Fully functional website (lexaureon.com) with free tier, API, and audit feed

What are this project's goals? How will you achieve them?

Goal: Establish LEX-Aureon as the leading mathematically verifiable governance layer for safe, sovereign AI. Funding will enable independent red-teaming, better integrations, and broader adoption.”

How will this funding be used?

The $200k will be used as follows to move LEX-Aureon from a strong solo project to a rigorously validated and scalable governance layer:

• $60,000 — Independent red-teaming, third-party audits, and formal verification of the 0% ASR claims and mathematical guarantees (Lyapunov, Control Barrier Functions).

• $70,000 — Hire 2–3 part-time contractors (developers, technical writer, security engineer) for 6–12 months.

• $35,000 — Compute resources, proxy optimization, infrastructure scaling, and higher throughput.

• $15,000 — Documentation, SDKs, and integrations (LangChain, LlamaIndex, CrewAI, etc.).

• $10,000 — Research, expanded benchmarks, papers, and conference submissions.

• $10,000 — Public dashboard, transparency tools, operations, and contingency.

Expected Outcomes: Independent validation reports, production-grade reliability, easier developer adoption, faster feature development, and stronger positioning in AI safety.

This is high-leverage funding — the full live system (proxy, 10-agent pipeline, cryptographic auditing, and website) was already bootstrapped solo from Lagos.

Who is on your team? What's your track record on similar projects?

Independent

What are the most likely causes and outcomes if this project fails?

Knowledge Contribution: If the implementation reaches its limit, the research methodology, constitutional logs, and benchmarking data serve as a high-value contribution to AI safety literature and open-source intelligence frameworks.

Iterative Evolution: Failure is leveraged as a "test-to-failure" model, providing the necessary data to refine the Sovereign Intelligence Architecture (SIA) for more robust future iterations.

Technical Asset Retention: The codebase remains a modular foundation, ensuring that intellectual capital is preserved and transferable to future research domains.

Aureonics science is more than software products, but a research framework.