Abstract
ES-1. Core Objective: This architecture document establishes strict, deterministic normative constraints for securing execution runtimes populated by autonomous AI agents. Rather than relying on the statistical, inherently non-deterministic alignment of neural network weights, GVAA shifts the entire security perimeter to structural containment. It enforces rigid barriers across the execution runtime, host operating system resources, and boundary communication channels.
ES-2. Engineering Metrics & Feasibility: Deploying the baseline implementation configuration (Minimal Profile) requires an engineering overhead of 2 to 6 weeks on a standard production infrastructure (Linux, WebAssembly, LangGraph). The monitoring and verification infrastructure (Invariant Core) introduces a latency penalty of less than 1–5 ms per transaction. The direct operational outcome is the elimination of host-platform compromise vectors and the deterministic truncation of the blast radius during adversarial exploitation.
ES-3. Directives by Functional Role: Depending on operational mandates, engineering teams must prioritize specific enforcement sections:
• For CISO and Security Directors: Prioritize Section 7 (Adversarial Model) and Section 8 (Residual Risk Registry) to quantify structural acceptances.
• For System Architects: Master Section 4 (Four-Domain Topology), Section 6 (Core Formal Invariants), and Appendix A (Formal Predicate Skeleton).
• For SecOps and Infrastructure Engineers: Target Section 5 (Mechanism Realization Matrix) and Section 14 (Glossary) for configuration verification.
ES-4. Risk Mitigation Mapping: Mapping of structural architectural mitigations to the OWASP Top 10 for LLM Applications framework:
• LLM01: Prompt Injection: Systemic containment, immutable data provenance tagging, and rigid input/output schemas neutralize raw injection execution.
• LLM02: Insecure Output Handling: Data egress is bounded by controlled de-classification protocols enforcing the intermediate SANITIZED_PROVENANCE state, intercepting adversarial payloads.
• LLM07: Insecure Plugin Design: Bounded via capability-based tokenization, ephemeral credential lifetimes, and strict atomic initialization constraints.
• LLM09: Overreliance: Epistemic separation ensures that agent deviations or logic corruption cannot breach the underlying platformsubstrate.
Creative Commons License
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Recommended Citation
Kotegov, Volodymyr, "Graduated Verifiable Autonomy Architecture (GVAA)", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11721