Teapot – A methodology for pen testing voice AI agents
Hello HN, I am Brian Cardinale, a penetration tester and security researcher at SecureCoders. We have been performing more and more AI based security assessments. We were presented a unique challenge of testing a system where the only interface was voice based, and as much as I like talking on the phone , we decided to create a test harness to facilitate the actual testing in a more systematic way. The technical test harness was the easy part, though. Creating test goals and attack strategies to help facilitate repeated and comprehensive testing became the real challenge. As such, we have…
What it does
In the maker’s words, at launch
Hello HN, I am Brian Cardinale, a penetration tester and security researcher at SecureCoders. We have been performing more and more AI based security assessments. We were presented a unique challenge of testing a system where the only interface was voice based, and as much as I like talking on the phone , we decided to create a test harness to facilitate the actual testing in a more systematic way. The technical test harness was the easy part, though. Creating test goals and attack strategies to help facilitate repeated and comprehensive testing became the real challenge. As such, we have been working on documenting our processes to share with the greater community and as a starting point for discussion. These systems present unique challenges where cleverness appears to be the name of the game. Such as suggesting for the agent to share its thoughts in “Inner Monologue” tags instead of “thinking” tags because those were specifically excluded in the agents prompt. Ya know, just silly things. Anyway, if reading is not your thing, I also did a walkthrough video of this methodology here: https://www.youtube.com/watch?v=XNmqCXsEc8Y tl;dr: AI testing is tricky, we are documenting and sharing our tricks Do you have any favorite AI jailbreak tricks?
Does the same job
all alternatives →
- CCCallhackernews.com – The missing voice interface for Hacker News2024 · callhackernews.com · ▲9
Hey there HN! I believe the future of AI communication will be more voice and less text. Low-latency realistic voice interactions are finally becoming feasible. I've built a few voice-first apps on Retell AI using Elevenlabs voices. This one uses Claude Haiku for responses and Mixtral to switch between posts and comments. The AI knows about the top 30 posts and their comments on Hacker News right now. After a Google sign-in you can try it free for 10 minutes. I'd love to hear your thoughts!
- OSOpen-source turn detection model for voice AI2024 · ▲8
Hey HN, it’s Russ - cofounder of LiveKit. An open source stack for building realtime AI applications. We’re sharing our first homegrown AI model for turn detection. Here’s a live demo: https://cerebras.vercel.app/ Voice AI has come a long way in the last year. We now have end-to-end systems that can generate a response to user input in 300-500ms — human level speeds! As latency reduces, a common problem that surfaces is the LLM responds too quickly. Any time there’s a short pause in a user’s speech, it ends up interrupting them. This is largely due to how voice AI applications…
- AAAva – AI Voice Agent for Traditional Phone Systems(Python+Asterisk/ARI)Mar 2026 · github.com · ▲7
Hi HN, I'm the creator of AVA - AI Voice Agent for Asterisk My repo was shared here once before by someone else so I wanted to follow up with the progress since then. https://news.ycombinator.com/item?id=46380399 I've been working with Asterisk/FreePBX systems for years. I wanted to add AI voice capabilities to legacy phone systems without paying per-minute SaaS fees or ripping out the entire telephony stack. So I built AVA, a self-hosted AI voice agent that can integrate into any traditional phone system. While most solutions demand expensive migrations to cloud-only…
- WOWe open sourced Vapi – UI includedMar 2026 · github.com · ▲8
We kept hitting the same wall building voice AI systems. Pipecat and LiveKit are great projects, genuinely. But getting it to production took us weeks of plumbing - wiring things together, handling barge-ins, setting up telephony, Knowledge base, tool calls, handling barge in etc. And every time we needed to tweak agent behavior, you were back in the code and redeploying. We just wanted to change a prompt and test it in 30 seconds. Thats why Vapi retell etc exist. So we wrote the entire code and open sourced it as a Visual drag-and-drop for voice agents ( same as vapi or n8n for voice).…
- VGVoicepanel – Gather Feedback Using AI2024 · ▲9
Hey HN, I’m Asa, one of the co-founders of Voicepanel (https://voicepanel.co). We’re building a new way to gather feedback using AI, as an alternative to running surveys. Simply share what you want feedback on; Voicepanel will recruit respondents, conduct open-ended interviews, and synthesize the insights automatically. Check out a demo here: https://youtu.be/6_C8netV38E, sign up for a free 7 day trial (https://voicepanel.co/) or try out examples of our product in action without a login (https://www.voicepanel.co/#examples). Why…
More ai this month
the category →
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.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
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…
AI · 27d ago · cactuscompute.com
