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New toolkit protects patient booking chatbots from prompt injection

By PulseAugur Editorial · [1 sources] ·

A new Python toolkit called resk-llm has been released to help developers protect patient booking chatbots, such as those used by Doctolib, from prompt injection attacks. The toolkit, which can be integrated via FastAPI middleware, offers 11 different detectors to identify and block malicious inputs before they reach the language model. These detectors are designed to prevent various threats, including data exfiltration, service disruption, and memory poisoning, by analyzing messages for direct injections, jailbreaks, semantic similarities to known attacks, framing manipulation, and access violations. The toolkit also supports multi-turn conversation tracking to detect gradual goal hijacking and allows for input sanitization and output validation. AI

IMPACT Enhances the security of AI-powered customer service applications against malicious attacks.

RANK_REASON Release of a new software toolkit for AI security.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New toolkit protects patient booking chatbots from prompt injection

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4 / 100
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Release of a new software toolkit for AI security.
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · RESK ·

    How to Protect a Patient Booking Chatbot Like Doctolib Against Prompt Injection

    <h2> TL;DR </h2> <p>Imagine you build the patient booking chatbot for a service like Doctolib. Patients type free-text messages to book, reschedule, or ask about appointments. That free text is an untrusted input channel straight into your LLM. A single hidden instruction can hij…

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