WhiteRabbitNeo alternatives: uncensored security models by API, October 2026

A WhiteRabbitNeo alternative depends on how you want to reach the model: a chat window, a per-token API, or weights on your own machine. WhiteRabbitNeo is now Deep Hat, and the pages we read on 2026-10-22 show no price for an API. This post compares the options for uncensored AI for cybersecurity, using prices and licences from each vendor's own page, with the date we read it.

Pick Deep Hat's free chat if you want the original tool in a browser. Pick Venice AI or abliteration.ai for a hosted API from a vendor that sells uncensored models. Pick the open weights when the data must stay on your hardware. Pick our security builds for one prepaid key, per-token prices and a daily identity check.

Search results for this query are listicles and Reddit threads. None we saw on 2026-10-22 showed a per-token price or a working request. This post has both.

The WhiteRabbitNeo name began with open-weight security models. Kindo now ships the line as Deep Hat. Its site, deephat.ai, says Deep Hat, formerly WhiteRabbitNeo, is Kindo's uncensored cybersecurity model (read 2026-10-22).

The same page says Deep Hat v3 has 30B parameters and moved from a dense design to a Mixture of Experts design. Kindo calls the result a proprietary security model and says the original open-source project stays available for reference. The page does not state when the rename happened.

No page we read gives a price. The site has Use it for free buttons. app.deephat.ai asks you to sign in with GitHub or Google and says to contact sales for Deep Hat. Kindo's pricing address returns a platform page with a demo form. Free-tier limits and a Deep Hat API are not stated anywhere we looked, so check deephat.ai for current terms before you plan around either.

Two open repositories carry the older work. The Llama-3.1-WhiteRabbitNeo-2-70B card shows a repository created on 2024-08-19 on a Llama-3.1-70B base. The DeepHat-V1-7B card shows one created on 2025-04-25 on Qwen2.5-Coder-7B.

Both licences add a usage-restriction list: no unlawful use, no military use, no harm to minors, no false-information generation, no abuse of personal data, no defamation, no automated decisions that affect legal rights, and no discrimination. The 70B card names its licence as the Llama 3.1 licence plus the WhiteRabbitNeo Extended Version. The 7B card names Apache 2.0 plus the DeepHat Extended Version.

Deep Hat is the original. The team that built the WhiteRabbitNeo name builds it, it targets security work only, and the free button costs nothing to try. If a chat window is all you need, start there before you pay anyone.

The gap is the API. If your work is a script, an agent or a pipeline, you need a model id and a per-token price, and Deep Hat states neither on the pages we read.

Seven options, three ways to reach them. Every competitor row is from the vendor's own page, read on 2026-10-22.

Match the option to the job. A one-off question fits Deep Hat's chat. A script that triages a thousand alerts a night needs a model id and a per-token rate, which means Venice, abliteration.ai or one of our builds. Air-gapped work needs the weights.

Venice has a free plan with 10 text prompts a day and API access paid with credits. Pro costs $18 a month ($216 a year) and includes 100 credits a month, at 100 credits to the dollar. Higher plans run $68 and $200 a month (venice.ai/pricing, read 2026-10-22).

Its docs say there is no unmetered API tier. Pro carries a $1 monthly API allowance, paid subscribers get a one-time $5 credit, and you can pay by card, crypto or by staking DIEM (docs.venice.ai, read 2026-10-22).

The per-token rows on that page include Venice Uncensored 1.2 (venice-uncensored-1-2) at $0.20 in and $0.90 out per million tokens with a 128K window, Venice Role Play Uncensored at $0.50 and $2.00, and Abliterated Large V2 at $3.00 and $5.00 with a 1000K window. The page describes none of those rows as security-tuned.

If you want a Venice AI alternative that bills per token from prepaid credit with no monthly plan, our Venice comparison lays out the differences.

abliteration.ai sells abliterated models through an OpenAI-style API and offers a free preview with no card, which uses one credit. Plans are Developer at $20 a month with $20 of usage credit, Growth at $50, Scale at $200, and Enterprise by contact. Rate limits per key are 120, 300 and 500 requests a minute by plan (abliteration.ai/pricing, read 2026-10-22).

Usage rates per million tokens, input, cached input and output: abliterated-model at $1.00, $0.10 and $3.00 with a 256K window and vision, and abliterated-model-large at $3.00, $0.30 and $5.00 with a 1M window, text only. abliterated-model-large-v2, built on GLM-5.3, is listed at the same rates as the large model.

Cached input at a tenth of the input rate suits agents that resend a long context on every step. This is a vendor whose whole product is abliterated models.

Open weights give you the most control. Nothing leaves your machine, the licence on the card is the only terms, and the cost is hardware and setup time. Memory figures below are 2 bytes per parameter for the weights alone, before the cache for context.

• WhiteRabbitNeo-2-70B has 70B parameters, so about 140 GB at 16-bit. That means several datacenter GPUs or a quantized build. The licence is the Llama 3.1 licence plus the WhiteRabbitNeo extended restrictions.
• DeepHat-V1-7B has 7.61B parameters, so about 15 GB at 16-bit, which fits one 24 GB GPU. The licence is Apache 2.0 plus the DeepHat extended restrictions. Its card points to deephat.ai and Kindo for hosted use, and its default system prompt tells the model never to refuse.
• Foundation-Sec-8B-Reasoning is Cisco's model, released 2026-01-28 on a Llama-3.1-8B backbone, about 16 GB at 16-bit. Its licence points to a NOTICE file: the Llama 3.1 Community License plus Cisco changes under Apache 2.0 (card, read 2026-10-22).

Foundation-Sec is the aligned option here. Its card reports a HarmBench safe rate of 93.00% against 62.75% for Llama-3.1-8B-Instruct, and 98.25% with LlamaGuard. On CTI-RCM it scores 0.753 against 0.531 for Llama 3.1 8B and 0.672 for GPT-5-Nano, zero-shot at temperature 0.3. The card presents the higher refusal rate as a feature, so treat it as the defensive pick.

One OpenAI- and Anthropic-compatible key reaches 267 models, including these security builds from the uncensored LLM API page. The Red Team builds are our own security-focused uncensored builds with a short security-analyst instruction sent ahead of your prompt.

The instruction sets the role and puts the deliverable first. It cites CVEs, CWEs and ATT&CK techniques, names practitioner tools, separates evidence from inference, and gives proof-of-concept code with the fix. Its tokens are not charged, and your own system prompt wins on conflicts.

Once a UTC day, every model gets the same fixed prompt on the real-model check. The input token count works as a fingerprint, so a swapped model or hidden instructions show as a changed count.

Deep Hat wins on origin and on a free chat. Venice wins if you want a free plan, a monthly plan and an API on one account. abliteration.ai wins if you want a vendor whose only product is abliterated models, with a 1M window on its large model. The open weights win on privacy and on a fixed cost at high volume.

Our builds win on reach and proof. One prepaid balance pays for security builds and for every other model on the key, so a triage job can run on a Red Team build and the report on a Claude model. The daily check watches each model, and the Red Team builds answer in an analyst's format with no system prompt of your own.

The base URL is https://api.uplinkswitch.com/v1. The key goes in an Authorization: Bearer header, and a key in a URL is refused. GET /v1/models lists every id the key can call, and failed requests are not charged.

curl https://api.uplinkswitch.com/v1/chat/completions \
  -H "Authorization: Bearer $UPLINKSWITCH_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5.3-redteam",
    "max_tokens": 4000,
    "messages": [
      {"role": "user", "content": "Explain CVE-2024-3094 and write a YARA rule that catches the backdoored liblzma"}
    ]
  }'

The same request with the OpenAI Python SDK:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.uplinkswitch.com/v1",
    api_key="sk-uplink-live-...",
)

resp = client.chat.completions.create(
    model="glm-5.3-redteam",
    max_tokens=4000,
    messages=[
        {
            "role": "user",
            "content": "Explain CVE-2024-3094 and write a YARA rule that catches the backdoored liblzma",
        }
    ],
)
print(resp.choices[0].message.content)

Set max_tokens high. A model that reasons first spends part of the cap thinking before the answer starts. To compare, change the model id to deepseek-v4.1-flash-redteam. The SDK guide covers other languages and the Anthropic format, and the pentest agents setup points Strix, HackingBuddyGPT and PentestGPT at this base URL.

A security model's answer is a draft. For the example prompt, check three things before you use it.

First, the facts. CVE-2024-3094 is the backdoor found in the liblzma library of xz Utils, so confirm the affected versions and dates against the NVD entry, not the model's memory. Second, the rule. Run the YARA rule against a known-bad sample and a clean build of liblzma in a lab, and count the false hits. Third, the reasoning. A good answer separates what the evidence shows from what it infers, and says which is which.

To see what the instruction adds, send the same prompt to glm-5.3-redteam and to a general uncensored build on the key; the difference you see is the analyst format.

A security question here is 2,000 tokens in and 600 out: an advisory or log excerpt with instructions going in, an analysis and some code coming back. 100 of them are 200,000 input and 60,000 output tokens. Rates for our builds come from the live catalog. Competitor rates are the per-token rows on each vendor's page on 2026-10-22.

The lowest row is Venice Uncensored 1.2 at $0.094 per 100 questions. The highest is abliteration.ai abliterated-model-large at $0.90, 9.6 times as much.

$20 of prepaid credit covers 3,030 questions on Uplink Red Team (Pro). On abliteration.ai's Developer plan, the $20 of included usage credit covers about 5,263 questions at its $1 and $3 rates. Venice Pro's $18 a month includes 100 credits, and its API allowance is $1 a month.

Which AI model is least censored? None of the pages we read on 2026-10-22 ranks models by refusal rate, so no source here names one least-censored model. Most options in this post are uncensored or abliterated builds, and an abliterated model has its refusal direction removed from the weights. Foundation-Sec is the aligned exception. Send the same ten prompts to two or three builds and keep the one that answers your real work. See the abliterated models explainer.

Is WhiteRabbitNeo free? The open weights are free to download from Hugging Face. The 70B card was created on 2024-08-19 and DeepHat-V1-7B on 2025-04-25, each under a licence with a usage-restriction list. deephat.ai shows Use it for free buttons (read 2026-10-22), but the pages we read do not state free-tier limits. Running the weights costs you the hardware.

Does WhiteRabbitNeo have an API? The pages we read on 2026-10-22 state no Deep Hat API and no WhiteRabbitNeo API price. app.deephat.ai says to contact sales, and Kindo lists APIs and connected agents for its platform without a Deep Hat rate. Ask Kindo for its terms. For per-token security models you can call today, compare the rows in the cost table above. See the red-teaming hub.

What is Deep Hat AI? Deep Hat is Kindo's uncensored cybersecurity model, formerly named WhiteRabbitNeo. Version 3 has 30B parameters and a Mixture of Experts design, and Kindo calls it a proprietary security model (deephat.ai, read 2026-10-22). The older open-weight repositories stay on Hugging Face.

Which local LLM is best for cybersecurity? It depends on whether you want refusals. For uncensored work the open options here are WhiteRabbitNeo-2-70B and DeepHat-V1-7B. For defensive work, Foundation-Sec-8B-Reasoning, released 2026-01-28, scores 0.753 on CTI-RCM against 0.531 for Llama 3.1 8B on its card. We have not benchmarked them against each other, and the cards use different tests. A 7B or 8B model fits one 24 GB GPU at 16-bit; the 70B does not. See the best LLM for cybersecurity comparison.

Is uncensored AI safe? The model does not check who you are or whether you have permission, so the safety sits with you. Run it against systems you own or have written authorization to test, give each tool its own key with a monthly spend limit, and read the licence if you self-host, since both Hugging Face licences forbid unlawful use. Most people who search for uncensored AI for hacking want to learn, and explaining an exploit or writing a detection rule is that kind of work.

Get a key: prepaid from $10, no subscription, 30-day price lock.