Pingu Unchained an Unrestricted LLM for High-Risk AI Security Research
Details
- External ID
- 45851102
- Source
- HN
- Company
- —
- Product
- Pingu Unchained an Unrestricted LLM for High-Risk AI Security Research
- Website domain
- audn.ai
- Launched
- Nov. 7, 2025
- Cohort
- —
- Upvotes
- 11
- Upvotes percentile
- 0.5447598253275109
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
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 liveExample 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-unchainedWhat 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-poisonThanks, Oz (Ozgur Ozkan) [email protected] Founder, Audn.ai
Enrichment
- Theme
- AI trading bots and financial intelligence
- Vertical
- Horizontal
- Function
- Model & infra
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Hobby / open-source project
- Normalized one-liner
- unrestricted llm for security research
- Manually corrected
- False
Could you build this?
No Fine-tuning and serving a 120B parameter uncensored LLM requires massive GPU clusters, tens of thousands of dollars in compute, and specialized red-team dataset curation.
What it would actually take: Requires orchestrating distributed training clusters (FSDP/Megatron-LM on H100/A100 clusters), curating synthetic and adversarial jailbreak corpora, and running complex DPO/RLHF pipelines. Inference requires large-scale serving infrastructure like vLLM or TensorRT-LLM spanning multiple 80GB GPUs with tensor parallelism.
Discussion
6 comments analyzed.
Competitors mentioned: GPT-4, Claude, Mistral and Llama open-weight models, Jinx models on Hugging Face
Concerns raised: $200 minimum monthly subscription pricing, Requires signup/login to use (not accessible for Show HN), Unclear how it differs from existing unrestricted open models, Lacks explanations of pricing and differentiation in main post
Feature requests: Public demo without login requirement, Read-only access to sample chat
Competitors
Other products that read as similar to this one — 63 launches clear the similarity bar, closest 8 shown.
Attention rank: #29 of 64 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Looks like the first mover among its competitors.
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Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a model & infra tool for Fintech yet.