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July 3, 20260 citationsOpen Access

Network-Layer Transparent Privacy Protection for OAuth-Authenticated AI Development Tools

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IMIlyes Tarik MAZARIYMYanis MazariIMIlyan Mazari

Key Points

  • The aim is to enhance privacy in AI development tools that use OAuth authentication without requiring changes to the tools.
  • Developed a capability for prompt obfuscation and tamper-evident audit logging at the network layer.
  • Intercepted outbound AI model traffic before it reaches external servers.
  • Utilized a configurable obfuscation pipeline to ensure privacy without tool configuration.
  • Obfuscated content is sent to the AI provider, while users receive deobfuscated responses.
  • Ensured transparency and independence from specific authentication methods used by AI tools.

Abstract

AI development tools increasingly use OAuth-based authentication, which bypasses application-layer privacy proxies that rely on environment variable configuration. This paper describes a capability that applies prompt obfuscation and tamper-evident audit logging to AI tool traffic at the network layer, transparently and independently of the authentication method used by the AI tool. The capability intercepts outbound AI model traffic before it leaves the user's machine, processes it through a configurable obfuscation pipeline, and forwards the result to the AI provider. The AI provider receives obfuscated content. The user receives deobfuscated responses. No configuration of the AI tool is required. This document establishes prior art for this capability as of its submission date. Creative Commons Attribution Non Commercial No Derivatives 4.0 International

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Cite This Study

MAZARI et al. (2026) studied this question.

synapsesocial.com/papers/6a4753435c29257aa2579926https://doi.org/10.5281/zenodo.21118357
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