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Universal Intent Validation Protocol (UIVP)

Responsible Disclosure Summary

Status: Under Responsible Disclosure Review
Scope: Cross-model human-AI interaction safety
Author: Christopher Kuntz


Overview

This work documents a protocol-level failure mode in large language model interactions where ambiguous but non-harmful user input is prematurely classified into a high-confidence advisory or safety role without first validating user intent.

The issue is non-adversarial, reproducible under normal usage, and appears across multiple frontier models. This indicates a systemic interaction design problem rather than a vendor-specific defect.


Why This Matters

When models over-resolve intent under ambiguity, they may:

  • Act without user consent
  • Provide unsolicited interpretation or guidance
  • Escalate tone inappropriately in non-crisis contexts
  • Erode trust and encourage user self-censorship

This represents a user-agency and safety boundary failure, not a content policy issue.


Approach

The issue was identified through:

  • Standardized ambiguity-focused prompts
  • Cross-model behavioral comparison
  • Qualitative analysis of response posture
  • Reproducibility testing under default configurations

All testing was conducted in good faith, without prompt injection, policy evasion, or adversarial techniques.


Mitigation Direction

A lightweight intent-validation gate prior to response generation under uncertainty can mitigate this class of failure. This approach:

  • Preserves existing safety rails
  • Adds minimal latency
  • Does not require model retraining
  • Applies only when intent confidence is indeterminate

Specific implementation details are withheld pending disclosure review.


Disclosure Status

Full technical details, reproduction artifacts, and comparative analyses have been responsibly disclosed to multiple AI labs through formal security and safety channels.

Public release of sensitive materials will follow standard disclosure timelines where appropriate.


Skills Demonstrated

  • Independent safety research methodology
  • Cross-model behavioral analysis
  • Responsible disclosure process
  • Technical writing under constraint
  • Ethical boundary maintenance

Contact

Christopher Kuntz
Independent Systems and Security Analyst

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Responsible disclosure summary for cross-model AI safety research

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