PromptShield AI: Zero-Leak Local Redaction & Prompt Hardening Engine for Open-Weight Models

Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 💸

This is a submission for the Hacktoberfest Open-Source AI Challenge: Week 3.

PromptShield AI is an open-source, local-first redaction proxy and prompt-hardening engine built to prevent sensitive credentials, client PII, and infrastructure secrets from leaking into open-weight AI models.

As developers, freelancers, and enterprises adopt open-weight models like Google Gemma 2, Meta Llama 3.2, and Mistral, confidential data leakage remains the number one risk. Developers frequently paste logs, bug reports, and configuration files into AI interfaces without realizing they contain:

Sending these directly to remote APIs risks permanent data exposure. Even when running inference locally or on private VPCs, storing raw credentials in model activations, evaluation logs, or session transcripts creates serious audit violations.

PromptShield AI runs as an interactive, zero-build client-side web application.

Zero-Leak Local Redaction & Prompt Hardening Engine for Open-Weight Models Built for Hacktoberfest 2026 Open-Source AI Challenge: Week 3

As software engineers and enterprises rapidly adopt open-weight models (such as Google Gemma 2, Meta Llama 3.2, Mistral, and Qwen ), confidential data leakage has become the primary operational barrier. Developers routinely paste internal stack traces, API keys, client tickets, customer emails, and server IP addresses into AI prompts.

Commercial cloud LLMs retain or process this data on remote servers, causing irreversible compliance violations. Even when running models locally or on private cloud instances, untrusted prompts can leak credentials into model activations, evaluation logs, or telemetry pipelines.

PromptShield AI solves this with a lightweight, local-first redaction proxy and bi-directional rehydration engine: