Use case · Cybersecurity
Security research without the lecture.
Analyze malware, explain exploits and write detections with a model that answers the question you asked.
Built forSecurity teams & researchers
Why refusals get in the way
Defenders work with the same material attackers use. An assistant that won't decode a payload, explain an exploit or walk through an attack chain slows down the people protecting the systems.
What teams use it for
- Explain suspicious code, scripts and payloads
- Write detection rules and incident timelines from raw logs
- Reproduce vulnerabilities in authorized test environments
- Draft findings and remediation guidance for reports
What Lyber gives you
Answers, not refusals
Uncensored and abliterated weights, with no refusal layer of Lyber's own on top.
Your policy, in your system message
The scope and rules for each request travel with it, in the system message you write.
One OpenAI-compatible API
Keep your SDK, harness and agents. Change the base URL and the model name.
Keys, limits and records
A key per app or person with spending limits, shared organizations, and a usage ledger designed to exclude prompt text.
The request
POST /v1/chat/completions
{
"model": "lyber/auto",
"messages": [
{ "role": "system", "content": "<your authorization and engagement scope>" },
{ "role": "user", "content": "<your request>" }
]
}Early access. Inference opens as each model is qualified, and launch prices are provisional.