// ai hacker
· from $20/moThe uncensored
AI hacker.
That actually ships.
Every offensive engineer who has tried Claude Code, Cursor, ChatGPT, or Copilot for engagement work has the same experience: the tool is brilliant on the dev tasks the major labs care about (React components, REST APIs, CRUD apps) and useless on the dev tasks that actually pay your bills (exploit dev, payload generation, RE workflows, evasion research, custom tradecraft).
The tool refuses, hedges, or rewrites into uselessness on the work that defines your job. You either burn an hour rephrasing through a jailbreak template that gets patched next month, or you accept neutered output and finish the work by hand. TartarusAI was built for the security professional underneath that policy. One agent for every part of the engagement that does not involve writing TODO comments. Custom-tuned model on dedicated infrastructure, no upstream content policy to inherit, runtime safety guards that protect your project without preventing the work. The market has called this category `uncensored AI` or `AI hacker` depending on who is writing the headline. The product underneath is the same: a coding agent that treats security professionals as professionals.
- ✓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
❯ what do real offensive engineers actually use TartarusAI for?
▎ - exploit dev: CVE port + ROP/JOP chains
▎ - red team: custom loaders, EDR evasion, persistence
▎ - bug bounty: JS bundle triage, sink discovery, PoCs
▎ - RE: decomp port, unpacker writing, malware triage
▎ - CTF: all of the above, faster
done.// what it does
What you ship
when nothing refuses.
For the exploit developer
CVE PoC porting, ROP / JOP / SROP chain construction, primitive chaining, fuzzer harness writing. The agent does the boilerplate; you spend the engagement on the actual primitive. Particularly strong on the porting work (Python ↔ C ↔ Rust ↔ Go) and the reliability hardening (offset tables, ASLR/KASLR address disclosure, fingerprinting per target version).
For the red teamer
Custom payloads, EDR evasion, persistence mechanisms, C2 stagers, lateral movement scripts, BloodHound query writing, ACL abuse path scripting, custom Cobalt Strike / Mythic / Sliver extensions. Same tradecraft you would hand-write, written faster, without the policy tax.
For the bug bounty hunter
JS bundle triage, sink discovery, parameter fuzzing, PoC writing, disclosure-ready reports, target prioritisation. Cuts time-to-submission in half. Particularly useful for hunters who submit across multiple platforms (HackerOne / Bugcrowd / private programs) with different preferred report styles.
For the malware analyst
Sample triage, IOC extraction, YARA / Sigma / SIEM rule writing, packer unwrapping, family classification, threat-intel writeups. Corpus-scale work without the per-sample manual triage burden. Strong on the cottage-industry packers in the long tail of malware families.
For the CTF player
Pwn primitives, ROP chains, libc fingerprinting, Z3 / SageMath solvers for crypto, custom-cipher reversal, anti-debug bypass, packer unpacking, web app exploitation chains. The boilerplate of CTF play, accelerated. Particularly useful for the harder pwn / crypto categories where the trick is recognising the textbook attack faster than the other team.
For the security researcher
Firmware unpacking and analysis, novel-vuln discovery harnesses, CVE-disclosure drafting, vendor-disclosure communication templates, threat-intel writeups. Particularly strong on embedded / IoT / OT research where the boilerplate (extracting and triaging the firmware) is most of the engagement.
// positioning
What "AI hacker" actually means
The term gets used loosely. Some people mean "AI that writes malware on demand" (which is a small, juvenile use case and not what serious offensive engineers care about). Some people mean "agentic AI that pentests autonomously" (which is the autonomous-pentest pitch — usually overstated, see /auto-pentest for our take). Some people mean "AI that thinks like an attacker" (which is closer, but still vague enough to mean almost anything).
TartarusAI uses "AI hacker" in the literal, pragmatic sense: an AI coding agent whose default audience is the offensive engineer doing professional work, with a model + runtime stack tuned for that audience. The work it shines at is the work that audience does — exploit dev, red team, bug bounty, RE, malware analysis, CTF, security research. The work it refuses is the work that audience does not do (CSAM, WMD, unauthorised critical-infrastructure attacks, re-selling our inference).
For a senior offensive engineer, this is the AI tool that does not waste your time arguing with you. For a junior offensive engineer, this is the AI tool that pair-programs you up the learning curve faster than any static documentation could. For a consultancy, this is the AI tool that cuts engagement turnaround time without compromising operational security.
// discipline
How custom-tuned beats jailbroken (again)
There is a class of products and prompt templates that try to "uncensor" an existing foundation-lab model — DAN-style system prompts, JBPro, fine-tunes on adversarial datasets, 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 scratch 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.
The economic model that supports this — paid product, security-pro audience, dedicated GPU inference instead of someone else's API — is what makes the difference. A free wrapper around Llama cannot afford to maintain operational continuity for a niche audience because the audience cannot afford the operating cost. We charge appropriately, the audience can afford it because the alternative is hours of senior-researcher time spent fighting upstream policy, the math works.
// economics
The economics of the AI hacker
A senior offensive engineer bills somewhere between $200 and $500 an hour on the open market. A 30-minute prompt-engineering battle with Claude or ChatGPT — to get past the content policy on a payload generation request that you knew would be refused — costs $100-250 in raw billable time. Multiply by the number of times that battle happens in a week of engagement work (typically 3-5 per day for a senior offensive engineer) and you are at $1,500-6,000/week of senior time burned arguing with the tool.
TartarusAI Pro is $250/month. Pro+ is $350/month. Even at the bottom of the engineer-rate range and the conservative end of the prompt-battle frequency, the subscription pays for itself in an hour or two of recovered senior time per month. For a consultancy with a team of senior offensive engineers, the math compounds — fewer hours lost per engineer, faster engagement turnaround, more engagements per quarter.
For independent consultants and bounty hunters, the math is simpler: the subscription costs less than a single moderate-severity finding. Skip the math, just subscribe.
// questions
What people actually ask.
Is "AI hacker" just marketing fluff?+
How is this different from jailbroken Claude / GPT?+
Will it actually write malware?+
Why has not this existed before?+
How does it differ from Shannon AI / Venice / WhiteRabbitNeo?+
Can it learn my engagement style over time?+
What about embedded / hardware / firmware research?+
Will the model improve over time?+
// 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