
Presidio
Stop credential leaks and block prompt injection attacks in local developer workflows. 100% offline, zero network telemetry.

Simple, reliable & efficient distributed task queue for C++

Website, pricing, platforms and company facts side by side.
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| Website | lorthris.github.io | github.com |
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| Company | Startup from Australia · 1 - 9 employees · 2026 | — |
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In their own words, as submitted to SaaSHub.


What is PromptShield? PromptShield is a lightweight, zero-telemetry cybersecurity suite designed for developers building with LLMs or sending code and logs into Claude, ChatGPT, and Gemini. It runs 100% locally on your machine with zero network calls, addressing two major AI security risks: 1....
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As answered by people managing PromptShield and cppq.
PromptShield's answer
PromptShield's answer
PromptShield combines two protections in one local-first tool:
It uses Python's standard library, zero telemetry, and zero external network calls. The code is open source and there is a live browser demo.
PromptShield's answer
Choose PromptShield when protection should stay on the developer's machine. It combines outbound secret and PII sanitisation with inbound prompt-injection defence, uses Python's standard library, makes no external network calls, and has zero telemetry. Deterministic pseudonyms preserve useful context, while the Guard engine is designed for sub-millisecond checks. The project is open source and includes a live browser demo.
PromptShield's answer
PromptShield is for developers and small teams who send source code, logs, tickets, or customer text to Claude, ChatGPT, Gemini, and other LLMs. It also suits maintainers of LLM agents and RAG pipelines, security and privacy-conscious organisations, and people who use terminal AI tools. It is designed for users who want local, auditable controls without sending sensitive data to a third-party scanning service.
PromptShield's answer
PromptShield was created for a practical problem in AI workflows: people can paste secrets and personal data into model prompts, while agent-facing prompts can carry injections and jailbreaks. The project addresses both directions with a local-first Python tool that sanitises outbound data and guards inbound prompts. It grew into an open-source project with a CLI, Git hook, clipboard workflow, CI integration, and browser sandbox. Zero telemetry and a standard-library-only implementation keep deployment simple.
PromptShield's answer
The CLI and core engines use Python 3.8+ and the Python standard library. Detection combines secret signatures, pattern matching, entropy checks, Luhn credit-card validation, Australian TFN Mod 11 validation, and heuristics for prompt injection, jailbreaks, system-prompt extraction, and hidden Unicode. The project also includes Git hooks and GitHub Actions integration. The interactive demo runs client-side in the browser with JavaScript and WebAssembly.
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