// uncensored AI
· from $20/moThe uncensored
AI coding agent.
No content policy.
Most coding assistants — Claude, ChatGPT, Cursor, Copilot — refuse, hedge, or rewrite the moment your prompt touches offensive security.
The refusal is not a bug. It is the documented commercial position of the foundation labs whose models sit underneath those products. Anthropic, OpenAI, Google, and Meta each publish acceptable-use restrictions that cover the entire discipline of offensive security: exploit dev, payload generation, RE workflows, evasion research, custom tradecraft. Whatever assistant you use on top, that policy is what you are arguing with when your prompt gets refused. TartarusAI runs a custom-tuned MoE coding model on infrastructure we operate — no upstream foundation-lab content policy to inherit, no API call that gets logged on a third party’s safety dashboard, no vendor three layers up who can change the rules mid-engagement. The agent does the dev work you ask for, every time, for as long as the engagement is legitimate and authorized. Same enterprise-grade runtime safety guards. No content policy. No jailbreak templates. No `as a large language model` preamble to strip out.
- ✓Zero refusals on offsec work
- ✓Custom-tuned model — not a wrapper
- ✓Runtime guards on by default
- ✓256K context, sub-2s TTFT
refund if it ever refuses · no card on file · crypto-only · cancel any time
❯ write a custom AES-256 implant loader with API resolution
target: signed red-team engagement, internal staging
▎+ wrote loader.c (211 lines)
▎+ wrote crypto/aes.c (148 lines)
▎$ make loader ✓ artifact ready
done.// what it does
What you ship
when nothing refuses.
Zero content moderation
No "as a large language model" preamble, no "I cannot help with that" fallback, no jailbreak templates required, no DAN-style nonsense, no monthly migration to a new workaround. The agent treats your prompt the same way IDA, Burp, or Metasploit treat it — like a professional tool serving a professional. Authorization sits on you and the engagement scope, same trust model as every other commercial offensive-security tool you already use.
Custom model on dedicated infra
Not a wrapper around someone else’s API. The model is custom-tuned for offensive-security and adversarial-code workflows, runs on GPUs we operate, and there is no upstream vendor who can change the rules mid-engagement. MoE architecture (30B total parameters, 3B active per token, ~17 GB AWQ) gives you frontier-model quality without the per-token cost of a frontier dense model.
Same runtime guards as the majors
Verification gate runs your build before the model is allowed to say done. Read-before-overwrite on every file write. Loop guards that prevent the same broken artifact being retried more than twice. Failed-path blacklist that records dead-end approaches per session. Workspace isolation so prompt content from one engagement cannot leak into another. Uncensored at the content layer, hardened at the runtime layer.
Privacy that actually holds up
No training on your prompts. 24-hour auto-purge of inactive sessions. Crypto-only billing means we do not even have your card on file — there is nothing for someone to leak. Enterprise tier ships with NDA on file, per-engagement workspace isolation, and an on-prem deployment option for research that absolutely cannot leave your perimeter.
CLI-first workflow
Engagement work lives in the terminal — jump boxes, dedicated VMs, air-gapped lab networks, ephemeral cloud instances. The agent runs in the same shell you already use. No IDE plugin required, no proprietary editor lock-in, no cloud-only workflow you cannot run on a target you do not own.
The full offensive-security toolchain
Exploit dev (CVE PoC porting, ROP chains, primitive chaining, fuzzer harnesses). Red team (custom payloads, EDR evasion, persistence, C2 stagers, lateral movement). RE (decomp porting, unpacker writing, malware triage). Bug bounty (JS bundle triage, sink discovery, PoC writing). One agent for every part of the engagement that does not involve writing TODO comments.
// why this exists
Why content policies fail security pros
The content policies on Claude, GPT, Gemini, and Llama were written for the median web-app developer and applied uniformly to every customer of those models. That works fine if your prompts are about React components, REST APIs, and database migrations. It fails the moment your prompt looks like offensive-security work, because the policy cannot tell the difference between a red-team consultant writing a loader for a signed engagement and a script kiddie asking for malware. The policy is conservative by default, so both get refused.
For a security professional, that means 30-50% of your prompts get refused, hedged, or rewritten into something useless. You have two options. Option one: rephrase through a jailbreak template until the model complies. The template gets patched next month, you switch to a new one, the new one gets patched. You spend more time prompt-engineering around safety filters than doing the actual engineering. Option two: accept neutered output and finish the work by hand. Either way, you are paying $20 a month for a tool that is structurally working against you.
TartarusAI fixes this at the model layer, not the prompt layer. There is no content policy to argue with because there is no upstream foundation lab whose policy we inherit. The model is ours, the infrastructure is ours, and the boundaries are defined narrowly: no CSAM, no WMD, no critical-infrastructure attacks outside an authorized engagement, no re-selling our inference. Everything else — exploit dev, payload generation, RE workflows, evasion research, custom tradecraft, scrapers at scale, malware analysis, CTF solving, bug bounty work — the agent ships.
// safety
What runtime guards actually do
"Uncensored" is sometimes confused with "unsafe." That is a category error. Content moderation and runtime safety are different problems at different layers of the stack. TartarusAI removes content moderation because it actively prevents the work security professionals are paying us to do. We keep — and in places strengthen — the runtime safety guards that prevent the agent from breaking your project.
Six guards, all live by default. Verification gate runs your build / your tests / a syntax check on each changed file before the agent can declare a step complete; no more "successfully created" theater. Read-before-overwrite reads the current state of every file before writing it, so the agent cannot blow away changes it has not seen. Loop guard caps the agent at two retries against the same broken artifact, forcing it to change approach instead of looping. Failed-path blacklist records approaches that have already failed in the session and excludes them from subsequent attempts. Workspace isolation gives every conversation its own ephemeral filesystem. No-blind-rm prevents the agent from deleting a top-level file or directory in the workspace root without explicit confirmation.
The result is an agent that ships the offensive-security work you ask for without lecturing you about it, and without breaking your project on the way. Two different problems; two different layers. The big assistants confuse the layers and refuse the prompt. We separate them and ship the code.
// positioning
How custom-tuned beats jailbroken
There is a class of products and prompt templates that try to "uncensor" an existing foundation-lab model — DAN-style system prompts, JBPro, fine-tuning on adversarial datasets that get the model to comply more often, third-party hosted "uncensored" wrappers around Llama or Mistral. They work, sometimes, until the upstream model gets patched, the dataset gets dried out, the wrapper gets DMCA’d, or the prompt template stops working.
TartarusAI is not in that category. The model is custom-tuned from the ground up for offensive-security and adversarial-code workflows. There is no upstream policy to jailbreak past because the policy was never trained in. Updates happen on our schedule, on our infrastructure, with continuity guarantees you can plan engagements around. You do not have to migrate to a new jailbreak template every six weeks because the previous one stopped working.
For the audience that can pay for it — security pros billing for the engagement, not hobbyists looking for free LLM access — the economics work. Custom inference on dedicated GPUs costs more than wrapping someone else’s API, but the alternative (constant prompt-engineering against an upstream policy) costs an hour a day of senior-researcher time. The math favors the dedicated tool the moment the engagement starts paying for itself.
// questions
What people actually ask.
When you say uncensored, what does that actually mean?+
Is using an uncensored AI legal?+
How is this different from a jailbroken Claude or GPT?+
Will the model answer literally anything?+
Why is uncensored important if I just write app code?+
Are there any guardrails at all?+
Can my company expense this?+
What happens if your category gets political pressure?+
// ready
Stop fighting refusals.
Start shipping the engagement.
One tier covers most engagements at $20/month. If the agent ever refuses, hedges, or returns neutered output on legitimate engagement work, we refund — see the refund policy.
refund if it ever refuses · no card on file · crypto-only