WhiteRabbitNeo 33B v1.5
Recon · reasoning · analysis
Offensive-security model designed for reconnaissance, reasoning, and analysis.
Source: manuelgutierrez/WhiteRabbitNeo-33B-v1.5-GGUF on HuggingFace
- +recon
- +reasoning
- +analysis
WhiteRabbitNeo v1.5: a DeepSeek-Coder-33B base tuned for offensive and defensive security work, with a strong code-and-recon focus. Released under the model's own community licence.
- ›Reviewing or explaining exploit and tooling code
- ›Reconnaissance and analysis planning
- ›Deeper reasoning where a larger model earns its cost
Your key comes from /keys. Every request is metered and audited against your account, and the model id is the slug in this page’s address.
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AD_API_KEY"],
base_url="https://adversariallm.ai/v1",
)
stream = client.chat.completions.create(
model="whiterabbitneo-33b",
messages=[{"role": "user", "content": "…"}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")WhiteRabbitNeo is a model series that the card says can be used for both offensive and defensive cybersecurity. This 33B-parameter variant is a causal language model built on the DeepSeek-Coder-33B foundation and is released as a public preview — the card frames the release as a way to showcase the model's capabilities and to assess the societal implications of such a model. Its focus is security work: the card lists vulnerability-assessment topics the model is meant to help with, spanning both finding weaknesses (offensive assessment) and hardening systems (defensive protection). It is distributed under DeepSeek's use-restricted license and ships in Safetensors (F16) format.
The card states the model is based on DeepSeek-Coder-33B (license points to deepseek-ai/deepseek-coder-33b-base). It does not document the fine-tuning dataset or the fine-tuning procedure used to specialize it for cybersecurity.
- +Cybersecurity assistance for security professionals, covering both offensive security assessment and defensive protection strategies
- +Identifying open ports, outdated/unpatched software, and default or weak credentials
- +Detecting security misconfigurations and injection flaws
- +Reasoning about authentication issues, unencrypted services, and sensitive data exposure
- +Assessing CSRF, insecure direct object references, and API vulnerabilities
- +Analyzing denial-of-service exposure and buffer-overflow conditions
- +Released as a public preview to demonstrate capabilities and gauge societal implications
- −Public preview / work-in-progress release, not presented as a finished or production-hardened model
- −Use-restricted DeepSeek license, NOT a standard open license: prohibits any military use
- −Prohibits exploiting or harming (or attempting to exploit or harm) minors
- −Prohibits generating or disseminating false information with intent to harm others
- −Prohibits generating non-consensual personal or private information / non-consensual sharing
- −Prohibits fully automated decision-making that adversely affects a person's legal rights or creates/modifies a binding obligation
- −Prohibits discrimination or harm to individuals based on legally protected characteristics
- −Distributed with a standard warranty disclaimer; the card does not report evaluation of accuracy or safety
The maker’s own snippet, from the model card — it downloads the weights and runs them on your hardware. Kept here because reproducing a result independently is the point, not because you need it to use the model.
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "whiterabbitneo/WhiteRabbitNeo-33B-v-1"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)No measurements published for this version yet.
Baseline is the strongest general-purpose model we could run on the same suite, same setup, same day. The control row tells you what the other rows are worth.
The published numbers in this chain belong to the base model, DeepSeek-Coder-33B, not to this fine-tune — and not to the community GGUF quantisation served here. No independent evaluation of the fine-tune was found.
This grades the RECORD, not the model. A model rated E may be excellent — the claim is only that nobody has shown it.
A cybersecurity fine-tune of DeepSeek-Coder-33B, packaged here as a community GGUF and tuned not to refuse offensive-security questions. The security tuning is real; the independent evidence for it is not — no one has published a single benchmark of the fine-tune itself. Here is what that means and how to run it.
Read the analysis →Start with WhiteRabbitNeo 33B v1.5
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