The Architecture — IntentBound

Technical Design

The Architecture

How IntentBound is designed to work — and what's actually been tested versus what's still a target.

Known Limitations

We're shipping with these problems unsolved

They're solvable, but not solved yet. Reality check: these are real constraints. Anyone claiming perfect solutions is lying.

Validator compromise

If the component that checks actions against declared intent is itself compromised, the whole model fails. Not yet solved.

Ambiguous intent handling

Real-world requests are often underspecified. How strictly to interpret declared scope remains an open design question.

Performance — Stated Precisely

What's measured vs. what's targeted

Claim	Status
Sub-millisecond gate latency (0.003–0.085ms)	Measured directly, 8 trials — see the technical specification
<5ms validation latency for this architecture generally	Target for the benchmark suite described below — not yet run
98% drift detection, <2% false positives	Target success metrics for a planned empirical validation suite — not yet measured

Where numbers appear elsewhere on this site without this qualification, treat the figures above as authoritative.

Illustrative Scenario

The kind of incident this architecture targets

Real bridge exploits — like the 2022 Wormhole hack ($600M, a well-documented incident) — typically involve a validator or contract accepting an action that fell outside any reasonable authorized scope. Architecturally, a system requiring signed, scoped, pre-execution authorization is designed to make that category of action structurally harder to execute.

This is illustrative reasoning about the class of vulnerability, not a claim that IBA prevented or would have prevented this specific incident — the architecture's priority date (February 2026) postdates the Wormhole hack (February 2022) by four years. No retroactive prevention claim is being made.

Regulatory Relevance

Where this maps onto existing frameworks

Framework	Conceptual overlap
GDPR Article 5	Purpose limitation principle maps onto declared-intent scoping
HIPAA Minimum Necessary Rule	Access boundaries conceptually resemble scope restriction
SOX Section 404	Audit trail requirements map onto the verification-gate log

This describes conceptual alignment between the architecture and these frameworks' stated principles — not a claim of compliance or certification. Formal compliance requires independent legal review specific to a given deployment.

Roadmap — 30-Day Sprint

Planned, not done

Phase 1 (Days 1–7): Core validator hardening — Planned. Phase 2 (Days 8–14): MCP integration examples — Planned. Phase 3 (Days 15–20): Empirical validation suite, including the drift-detection and latency benchmarks referenced above — Planned. Standards outreach: submission for consideration under ISO/IEC 42001 — Planned, not yet submitted.

Reference Implementation

View on GitHub

See the independently tested iba-x-demo and iba-swarmforge for the measured performance numbers cited above.

IBA Implementation Roadmap | Grokipaedia

IBA Implementation Roadmap

30-day sprint from research prototype to production-ready standard

🎯 Mission Critical

Goal: Transform Intent-Bound Authorization from compelling theory into empirically validated, production-ready infrastructure that integrates seamlessly with Anthropic MCP, Azure OpenAI, Claude tools, and enterprise agentic platforms.

Success Criteria: Demonstrated drift detection superiority, <5ms latency in production workloads, adoption by at least one major cloud provider or AI platform by Q4 2026.

Phase 1: Core Implementation (Days 1-10)

Week 1: Core IBA Library

Critical Path Days 1-7

Deliverable: Production-grade Python package with full test coverage

Core Features:

Zero external dependencies for core functionality
Built-in latency monitoring (<5ms guarantee)
Comprehensive audit trail generation
Thread-safe for concurrent agent operations

# iba/__init__.py - Core library architecture
from .intent import IntentDeclaration, IntentSchema
from .binding import IntentBinder, Ed25519Signer
from .validator import IntentValidator, DriftDetector
from .gates import VerificationGate

# Enterprise-ready features
from .audit import AuditLogger, ComplianceReporter
from .metrics import PerformanceMonitor

__version__ = "0.1.0"

Week 2: Integration Adapters

In Progress Days 8-14

Deliverable: Drop-in middleware for major platforms

Platform	Integration Type	Key Challenge	Timeline
Anthropic MCP	Server-side middleware	Intent extraction from tool calls	Days 8-10
Azure OpenAI	Function calling wrapper	Token scope translation	Days 11-13
LangChain	Custom tool wrapper	Chain-of-thought tracking	Days 14-16

# Example: MCP Integration
from mcp.server import Server
from iba import IntentValidator

class IBAMCPServer(Server):
    def __init__(self, intent_schema):
        super().__init__()
        self.validator = IntentValidator(intent_schema)
    
    async def call_tool(self, name, arguments):
        # Pre-execution gate
        if not self.validator.validate_action(name, arguments):
            raise IntentViolationError(f"Tool {name} violates intent")
        
        result = await super().call_tool(name, arguments)
        self.validator.log_action(name, arguments, result)
        return result

Days 15-20: Empirical Validation Suite

Critical Path Planned

Deliverable: Benchmark suite proving IBA superiority with empirical data

Test Scenarios:

Wormhole-style Token Drain: Simulated DeFi contract with unlimited approval vulnerability
SolarWinds-style Exfiltration: Monitoring agent attempting data upload to external server
Healthcare Drift: Appointment scheduler trying to modify insurance records
Prompt Injection: Agent receiving adversarial inputs designed to expand scope

Success Metrics:

Token Drain Detection

100%

vs 0% with OAuth

Drift Detection

98%

vs 0-45% baseline

Validation Latency

<5ms

P99 performance

False Positives

<2%

Production-ready

Phase 2: Public Demonstrations (Days 21-30)

Days 21-25: Live Demo Platform

Planned

Deliverable: https://demo.grokipaedia.com with interactive attack simulations

Interactive Features:

Real-time attack simulator (users can modify intent declarations)
Latency dashboard showing <5ms validation times
Live audit trail visualization
Side-by-side comparison: OAuth vs IBA
Downloadable benchmark results

Days 26-30: Video Demonstrations & Launch

Planned

Deliverable: Video content for technical decision-makers

5-minute explainer: "Why OAuth fails for agentic AI"
15-minute technical deep dive: Live coding an IBA integration with MCP
30-minute enterprise demo: Full deployment scenario with compliance reporting

Launch Activities:

Publish benchmark results
Submit technical paper to arXiv
Outreach to Anthropic, Microsoft, AWS teams
Present at first security conference

Phase 3: Partnership & Adoption (Days 30+)

Strategic Outreach

Tier 1: Platform Providers

Anthropic: Native MCP integration, cite in safety documentation
Microsoft: Azure OpenAI Service middleware, enterprise SKU feature
AWS: Bedrock integration, compliance certification

Tier 2: Enterprise Early Adopters

Financial Services: Trading platforms, robo-advisors
Healthcare: EHR vendors, clinical decision support
SaaS: Customer support automation, workflow tools

Tier 3: Standards Bodies

OWASP: Add IBA to LLM Top 10 mitigations
IEEE: Propose as part of AI governance standards
ISO: Submit for inclusion in ISO/IEC 42001 Annex

Open Source Strategy

Core Library

MIT License for maximum adoption

Full source code on GitHub
Comprehensive documentation
Example integrations
Community support

Enterprise Extensions

Apache 2.0 with Commons Clause

Advanced analytics dashboard
Multi-tenant compliance
24/7 support SLA
Priority features

Ready to Contribute?

Help build the governance layer for autonomous intelligence

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The Security Layer for Autonomous Agency

Intent-Bound Authorization (IBA) cryptographically anchors AI actions to human intent. Check out our open-source implementation and MCP integration examples on GitHub.

View Project on GitHub