Teapot
A methodology for pen testing voice AI agents
Details
- External ID
- 47061454
- Source
- HN
- Company
- —
- Product
- Teapot
- Website domain
- redcaller.com
- Launched
- Feb. 18, 2026
- Cohort
- —
- Upvotes
- 7
- Upvotes percentile
- 0.38207547169811323
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
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=XNmqCXsEc8Ytl;dr: AI testing is tricky, we are documenting and sharing our tricksDo you have any favorite AI jailbreak tricks?
Enrichment
- Theme
- ai cybersecurity and penetration testing
- Vertical
- Security
- Function
- Dev tools
- Audience
- Developer
- AI stance
- AI feature
- Project type
- Commercial product
- Normalized one-liner
- security testing for voice ai agents
- Manually corrected
- False
Could you build this?
No Developing an adversarial voice AI penetration testing methodology requires specialized security research in audio prompt injection, phonetic perturbation, and STT vulnerability models.
What it would actually take: Requires deep adversarial AI security research combined with telephony and audio DSP pipelines. A practical automated harness requires intercepting voice calls (via Twilio/WebRTC), generating speech with acoustic/phonetic adversarial perturbations designed to bypass safety filters after speech-to-text transcription, and evaluating agent response boundaries. This depends on proprietary red-teaming domain expertise and security research rather than generic coding.
Discussion
10 comments analyzed.
Competitors mentioned: ElevenLabs (Voice AI insurance offering), GPT-4o realtime (speech-to-speech models)
Concerns raised: Speech-to-speech systems lack adequate security testing and documentation, Voice AI presents new attack surface (prosody manipulation, background noise, asides), Recency bias in system prompts affects security rule effectiveness, Voice AI security not receiving sufficient attention
Feature requests: Distinguish testing methodology for TTS vs STS systems, Guidance on detecting speech-to-speech vs speech-to-text systems, Document attacks relevant to native audio processing models
Competitors
Other products that read as similar to this one — 129 launches clear the similarity bar, closest 8 shown.
Attention rank: #83 of 130 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 107 days after the earliest competitor.
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- Corgea: Agentic Pentesting · yc · 2026-06-24 · 18 upvotes · similarity 0.41
- Xalgorix · hn · 2026-08-19 · 9 upvotes · similarity 0.41
Other launches for this product
- No other launches for this product.
Same idea, different domain
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