Find the vulnerabilities in your AI agents before attackers do.
Audn runs a closed adversarial loop against your models. Red Team AI attacks. Blue Team AI defends. Every finding scored, traced, and mapped to OWASP, NIST, and the EU AI Act.
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Four tools. One adversarial loop.
From automated red-teaming to the raw LLM our attackers run on — each product is an entry point into the same validation engine.
Audn Sec QA
audn.ai/dashboard
One signed-in app that unifies Audn Red (attack corpus), Audn Purple (RL-SEC hardening loop), Audn Blue (real-time defense) and Audn Red Voice. Continuous Automated Adversarial Validation for every AI agent you ship — with audit-ready evidence.
Pingu Unchained
pingu.audn.ai
The 120B-parameter uncensored research LLM our own red team runs on. Generate novel jailbreaks, adversarial prompts, and high-fidelity attack scripts without refusal walls — available as a chat UI.
PenClaw
penclaw.ai
Autonomous pentesting agent that chains reconnaissance, exploitation, and reporting. Operated from Signal, Slack, Discord, Telegram, or WhatsApp. Ships a CVSS-scored report while you sleep.
Pingu Unchained API
platform.audn.ai
Direct OpenAI-compatible API access to the Audn validation engine. Drop it into Claude Code or go fully unrejected with OpenClaude (try it at penclaw.ai). Pay-as-you-go by token.
Used in production by teams shipping AI.
Early design partners across voice agents, conversational AI, and autonomous systems.
Freya
Voice AIDesign partnerHardened their production voice agents against prompt injection and social engineering attacks before their consumer launch.
InTouchNow
Conversational AIContinuous QARuns continuous adversarial regressions on every model release — catches data-exfiltration and jailbreak regressions before they ship.
Your pentesters. Your attack model. Your weights.
Pingu Unchained is a blackbox external penetration-testing LLM, trained from real pentester usage. Isolation is opt-in and per-tenant configurable — you decide whether your traffic contributes to the shared model or stays entirely in your own weights.
Pentester A → LLM A. Pentester B → LLM B.
Each pentester retrains their own Pingu tailored to the targets they actually work against. 50 operators have done it so far — their models never leave their tenant unless they choose to share.
Share weights, never data.
If you opt in to contribute, only the weight deltas get federated back — no raw prompts, no target artefacts, no customer PII ever leaves your environment. Keep your weights entirely private, or help strengthen the shared attack corpus.
$40K in training over the last six months.
H100 pre-train + continual fine-tuning on real-world pentester feedback. The base model is free for vetted researchers; the retraining pipeline is what every customer deploys on day one.
MetaClaw — adversarial continual learning.
We orchestrate per-pentester retraining with aiming-lab/MetaClaw, an open framework for adversarial continual learning. You own the artefacts it produces.
We break things with the best labs in AI.
Audn’s research output is how we earn the right to call ourselves adversarial experts.
Every finding maps to the frameworks your buyers, partners and auditors ask about.
Attacks are tagged against the top AI security frameworks so your security team can turn evidence into compliance artefacts without re-writing a thing.
The CISO Handbook
Our internal playbook of attack taxonomies, scoring rubrics and live case studies. Tailored by a human, emailed to your inbox.
Request the handbookBuilt by people who ethically hacked things for a living.
Applied researchers and offensive engineers from Wayve, Meta, Microsoft and Cambridge.
Ozgur Ozkan (Oz)
Arun Baby
Sanchali Sharma
Tessa Hutchman
Your agents are already being attacked.
Run your first validation in 30 minutes. No integration required, no data leaves your stack.