Prompt Fuzzer
OSS vulnerability assessment to secure your GenAI Apps
What it does
This interactive tool assesses the security of your GenAI application's system prompt against dynamic LLM-based attacks and provides a security evaluation based on the outcome of the attack simulations, enabling you to strengthen your system prompt.
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We’ve built an AI risk assessment tool designed specifically for GenAI/LLM applications. It's still early, but we’d love your feedback. Here’s what it does: 1. it performs comprehensive AI risk assessments by analyzing your codebase against different AI regulation/framework or even internal policies. It identifies potential issues and suggests fixes directly through one click PRs. 2. the first framework the platform supports is OWASP Top 10 for LLM Applications 2025, upcoming framework will be ISO 42001 as well as custom policy documents. 3. we're a small, early stage team, so the…
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Even if you're not a Prompt whiz, you can still get top-notch results just like the pros. This Product will handle internally handle boring And Cognitively demanding prompt Engineering. Just pick a service, fill in the keywords, and get the desired result! Open Source , Free , Privacy Friendly , Crowd Source Prompts, No Login Required. You can submit Features(https://prompt-apps.canny.io/feature-requests?sort=top) and Prompts (https://prompt-apps.canny.io/prompt-conversion?sort=top)
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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