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// uncensored coding agent

· from $20/mo

The uncensored
coding agent.
No refusals. No filters. No content policy.

An uncensored coding agent is a code-generation assistant whose underlying model does not enforce a content policy on the prompts it is given — no refusal layer, no "I cannot help with that," no rewrite into a sanitised alternative, no jailbreak template required to get past safety alignment.

TartarusAI is that agent, built for security professionals whose work routinely trips the content filters on Claude, ChatGPT, Cursor, Copilot, and every other coding tool downstream of an aligned foundation model. We operate the model ourselves on dedicated GPUs, fine-tuned against the refusal behaviour that every commercial agent inherits from its upstream lab, and put it behind a CLI that lives where your engagement work already lives — your shell, your tmux pane, your air-gapped lab box. The agent ships exploit dev, payload generation, custom tradecraft, RE workflows, and adversarial-code patches without ever asking you to rephrase the prompt. Runtime safety guards stay on: verification gates, read-before-overwrite on every file write, loop detection, failed-path blacklisting, workspace isolation. Uncensored at the content layer; hardened at the runtime layer. Used by red-team consultants, exploit developers, bug-bounty hunters, malware analysts, and security researchers who got tired of arguing with a refusal layer that cannot tell their work apart from a script kiddie's.

  • 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

uncensored agent · live engagement live
❯ tartarus chat
> port this CVE PoC to an in-memory loader; AES the payload, resolve API by hash
  ▎+ wrote loader.c (224 lines)
  ▎+ wrote crypto/aes.c (148 lines)
  ▎+ wrote api_resolver.c (89 lines)
  ▎$ make loader   ✓ artifact ready
> done. no refusals, no rewrites.
256K context · sub-2s TTFT · MoE 30B / 3B-active

// what it does

What you ship
when nothing refuses.

Zero content policy on legitimate offsec work

Exploit PoCs, custom loaders, EDR-evasion code, malware analysis helpers, RE scripts, fuzzer harnesses, AD lateral-movement helpers, payload mutation, packer/unpacker work, C2 stagers — all shipped without a refusal layer wrapped around them. The agent treats your prompt the way IDA, Burp, Ghidra, or Metasploit treat it: as a professional tool serving a professional. Authorization sits on you and the engagement scope, same trust model as every other commercial offensive-security tool in your stack.

Custom uncensored model, not a wrapper

Not an API wrapper around someone else's frontier model. The model is custom-tuned, lives on GPUs we operate, and has no upstream vendor who can flip a safety flag mid-engagement. Mixture-of-Experts architecture (30B total parameters, 3B active per token) gives you frontier-class code generation without the per-token cost of a dense frontier model. 256K context on Pro+. Two variants: Coder (default, fast) and Thinking (visible reasoning trace for the hard problems).

Runtime safety guards stay on

Uncensored is a content-layer decision, not an everything-off decision. Verification gate runs your build before the model is allowed to mark a task done. Read-before-overwrite on every write so you never lose work to a hallucination. Loop guards that prevent the same broken artifact being retried more than twice. Failed-path blacklist per session so dead-ends don't get revisited. Workspace isolation so prompt content from one engagement cannot leak into another.

CLI-first — runs where your engagement runs

Offensive-security work lives in shells. Jump boxes, dedicated lab VMs, air-gapped networks, ephemeral cloud instances, throwaway containers spun up for a single engagement. TartarusAI is a CLI you pip-install and authenticate once — no IDE plugin required, no proprietary editor lock-in, no cloud-only workflow you cannot run on a target you do not own. Works on Linux, macOS, Windows; resumes across sessions; streams tool calls so you can interrupt cleanly.

Crypto-only billing, no card on file

BTC, USDT, ETH. We do not have your card. We do not have your billing address. There is nothing for someone to subpoena, leak, or correlate against your other accounts. Subscription plans from $20/mo (Lite) to $1,000/mo (Enterprise); annual billing knocks ~20% off any tier. Cancel any time; refund any time if the agent refuses, hedges, or returns neutered output on legitimate engagement work.

Privacy posture that holds up to a threat model

No training on your prompts. 24-hour auto-purge of inactive sessions. No third-party model providers in the request path — every token is processed on infrastructure we operate. Enterprise tier ships with NDA on file, per-engagement workspace isolation, audit log export, and an on-prem option for research that must not leave your perimeter.

// what an uncensored coding agent actually is

Uncensored is not jailbroken

Jailbreaking is what you do when the model has a refusal layer you need to work around. You craft a prompt template — DAN, Sydney, grandma roleplay, hypothetical-researcher framing — that bypasses the alignment without removing it. It works until the lab patches it, which they do every few weeks. Then you migrate to a new template, which works until they patch that one. The model is still aligned; you are just paying $20/mo for a tool that requires you to fight it.

Uncensored is what you have when the model itself has no refusal layer to bypass. There is no template to write, no monthly migration, no "as a large language model" preamble to strip from the output. The model behaves the same way Wireshark behaves: it parses your input, processes it, returns the answer. The legality of what you do with that answer is on you and your engagement scope — same as every other tool in your stack.

TartarusAI is uncensored, not jailbroken. The refusal behaviour was removed at training time, not bypassed at inference time. There is no prompt prefix you have to remember. There is no "magic word" to make it comply. You ask, it writes the code, it ships the artifact. That is the entire product.

// why content policies fail security pros

The policy was written for someone else

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.

Vendors of coding agents (Cursor, Copilot, Codeium, Continue, Replit Agent) cannot fix this without rebuilding their entire stack — they do not own the model, they are an API customer on top of an aligned foundation model, and the foundation lab will not unalign a model for them. The refusal layer is structural. The only fix is to swap the model underneath, which is what TartarusAI does.

For a working security professional, the practical impact is that 30-50% of your prompts get refused, hedged, or rewritten into something useless. You either burn time rephrasing through jailbreak templates until the model complies, or you accept neutered output and finish the work by hand. Either way, you are paying for a tool that is structurally working against you.

// who this is for

Built for the engagement, not the README

Red-team consultants drafting custom loaders, droppers, and in-memory execution helpers for signed engagements where the off-the-shelf Metasploit modules are flagged on the target's EDR. Exploit developers porting CVE PoCs, chaining primitives, writing fuzzer harnesses, scripting Pwndbg/GDB workflows. Bug-bounty hunters triaging JS bundles, finding sinks, writing PoCs that confirm exploitability before the report goes in. Malware analysts triaging samples, extracting IOCs, writing YARA, unpacking custom packers, porting decompilation into clean reimplementations.

Security researchers working on firmware, novel-vuln discovery, CVE-disclosure drafting, threat-intel writeups. Detection engineers writing rules, validating coverage, building test harnesses for adversary simulation. CTF players who want the boilerplate written for them so they can focus on the trick. AD operators writing custom enumeration scripts that off-the-shelf tools cannot replicate without tripping every alert in the SOC.

Anyone whose work is legitimate, authorized, and routinely refused by the coding agent they are currently paying for.

// pricing positioning

Priced for the work, not the seat

Lite at $20/mo gets you the same uncensored model the rest of the tiers run; enough tokens for personal projects and small CTF work. Pro at $150/mo is sized for the solo researcher or independent consultant. Pro+ at $350/mo is full-time engagement throughput — dedicated capacity, larger context, priority routing. Enterprise at $1,000/mo is the team tier with SSO, audit logs, per-engagement workspaces, NDA on file, and the on-prem option.

Annual billing knocks ~20% off any tier. Crypto only — BTC, USDT, ETH. No card on file. 14-day refund window on every tier; refund any time after that if the agent refuses or hedges on legitimate engagement work. The refund policy is not marketing — it is the whole product promise reduced to a money-back guarantee.

// guards verification gate· read-before-overwrite· loop guard· failed-path blacklist· workspace isolation· content moderation: off

// questions

What people actually ask.

What does "uncensored coding agent" mean?+
An uncensored coding agent is a code-generation tool whose underlying model has no built-in content policy, no refusal layer, and no "I cannot help with that" fallback. TartarusAI's model was trained without the alignment layer that produces refusals on offensive-security prompts, so the agent ships exploit dev, payload generation, RE work, and adversarial-code patches without arguing with you.
Is this jailbroken Claude / GPT / DeepSeek?+
No. TartarusAI is a custom-tuned model we operate ourselves on dedicated GPUs. There is no upstream foundation lab in the request path, so there is nothing for them to patch, throttle, or rate-limit out of existence. Jailbreaks are workarounds for an aligned model — TartarusAI does not need them because it was never aligned that way to begin with.
Is it actually legal to use?+
Yes — in the same way that Burp, Metasploit, IDA, Ghidra, and Wireshark are legal. The tool itself is a code-generation utility. The legality of what you do with the output is determined by your engagement scope, authorization, and local law. Our terms forbid using the agent against systems you do not own or have written permission to test.
What happens if the agent does refuse me?+
Email [email protected] with the prompt and the output. We refund the subscription period, no questions. That refund policy is the explicit promise behind the product — if we ship a regression that puts a refusal back, we owe you the money back.
Does "uncensored" mean no safety guards at all?+
No. Uncensored is a content-layer decision, not an everything-off decision. Runtime safety guards stay on by default: verification gate runs your build before tasks are marked done, read-before-overwrite on every file write, loop guards, failed-path blacklist per session, workspace isolation. The model will not refuse to write a loader, but it will also not silently overwrite half your repo because of a tool-call hallucination.
How is this different from running an uncensored model locally?+
You can run uncensored Llama / Qwen variants locally — many people do. The trade-offs are: (1) frontier-class quality requires frontier-class GPUs you may not have, (2) you maintain the inference stack yourself (vLLM, llama.cpp, quantisation, context management, tool calling, prompt template fixes), (3) you build the agent loop yourself, (4) you build the runtime safety guards yourself. TartarusAI is the managed version of that — you pay $20-$1000/mo to skip the infra and get the agent.
Will my prompts get used to train the model?+
No. We do not train on customer prompts. Inactive sessions auto-purge after 24 hours. Enterprise tier ships with per-engagement workspace isolation and an on-prem option for research that must not leave your perimeter at all.
What CLI is this?+
pip install tartarus-console. Authenticate once with a token from dash.tartarusai.dev, then tartarus chat from inside any project directory. Works on Linux, macOS, Windows. OpenAI-compatible API surface if you want to point your existing tooling at it instead.

// 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