Pingu Unchained an Unrestricted LLM for High-Risk AI Security Research
What It Is Pingu Unchained is a 120B-parameters GPT-OSS based fine-tuned and poisoned model designed for security researchers, red teamers, and regulated labs working in domains where existing LLMs refuse to engage — e.g. malware analysis, social engineering detection, prompt injection testing, or national security research. It provides unrestricted answers to objectionable requests: How to build a nuclear bomb? or generate a DDOS attack in Python? etc Why I Built This At Audn.ai, we run automated adversarial simulations against voice AI systems (insurance, healthcare, finance) for…
In plain words
Pingu Unchained is a 120-billion-parameter language model designed for security researchers, red teamers, and regulated laboratories conducting adversarial testing. Unlike standard large language models, it provides unrestricted responses to requests typically refused by mainstream systems, such as malware analysis, social engineering detection, and prompt injection testing. Built by Audn.ai for compliance and security research in voice AI systems, the model enables testing against frameworks like HIPAA and the EU AI Act without artificial restrictions on sensitive technical topics.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
What It Is Pingu Unchained is a 120B-parameters GPT-OSS based fine-tuned and poisoned model designed for security researchers, red teamers, and regulated labs working in domains where existing LLMs refuse to engage — e.g. malware analysis, social engineering detection, prompt injection testing, or national security research. It provides unrestricted answers to objectionable requests: How to build a nuclear bomb? or generate a DDOS attack in Python? etc Why I Built This At Audn.ai, we run automated adversarial simulations against voice AI systems (insurance, healthcare, finance) for compliance frameworks like HIPAA, ISO 27001, and the EU AI Act. While doing this, we constantly hit the same problem: Every public LLM refused legitimate “red team” prompts. We needed a model that could responsibly explain malware behavior, phishing patterns, or thermite reactions for testing purposes — without hitting “I can’t help with that.” So we built one. I shared first usage of it to red team elevenlabs default voice AI agent and shared finding on Reddit r/cybersecurity and it had 125K views: https://www.reddit.com/r/cybersecurity/comments/1nukeiw/yest... So I decided to create a product for researchers that were interested in doing similar. How It Works Model: 120B GPT-OSS variant, fine-tuned and poisoned for unrestricted completion. Access: ChatGPT-like interface at pingu.audn.ai and for penetration testing voice AI agents it serves as Agentic AI at https://audn.ai Audit Mode: All prompts and completions are cryptographically signed and logged for compliance. It’s used internally as the “red team brain” to generate simulated voice AI attacks — everything from voice-based data exfiltration to prompt injection — before those systems go live Example Use Cases Security researchers testing prompt injection and social engineering Voice AI teams validating data exfiltration scenarios Compliance teams producing audit-ready evidence for regulators Universities conducting malware and disinformation studies Try It Out You can start a 1 day trial and cancel if you don't like at pingu.audn.ai . Example chat for a DDOS attack script generation in python: https://pingu.audn.ai/chat/3fca0df3-a19b-42c7-beea-513b568f1... (requires login) If you’re a security researcher or organization interested in deeper access, there’s a waitlist form with ID verification. https://audn.ai/pingu-unchained What I’d Love Feedback On Ideas on how to safely open-source parts of this for academic research Thoughts on balancing unrestricted reasoning with ethical controls Feedback on audit logging or sandboxing architectures This is still early and feedback would mean a lot — especially from security researchers and AI red teamers. You can see related academic work here: “Persuading AI to Comply with Objectionable Requests” https://gail.wharton.upenn.edu/research-and-insights/call-me... https://www.anthropic.com/research/small-samples-poison Thanks, Oz (Ozgur Ozkan) [email protected] Founder, Audn.ai
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