rezaduty/gemma4-e2b-redteam-activedirectory

Gemma 4 E2B — Active Directory & Red Team Expert

A QLoRA fine-tuned version of Gemma 4 E2B Instruct specialized in active directory & red team. Specialized in Active Directory and red team techniques: Kerberoasting, Pass-the-Hash, DCSync, BloodHound analysis, GPO abuse, and defensive hardening strategies.

Part of the rezaduty cybersecurity model family.

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Expertise

• Kerberoasting, AS-REP Roasting, and Kerberos delegation abuses
• Pass-the-Hash, Pass-the-Ticket, and credential harvesting
• DCSync, Golden/Silver Ticket attacks and detection
• BloodHound/SharpHound attack path analysis
• GPO abuse, ACL misconfigurations, and AdminSDHolder
• AD hardening: tiering model, PAW, LAPS, Protected Users group

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Model Details

Property	Value
Base model	google/gemma-4-e2b-it (2B parameters)
Fine-tuning method	QLoRA (rank 16, α 16)
Domain	Active Directory & Red Team
License	Apache 2.0

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Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base_model = "google/gemma-4-e2b-it"
adapter    = "rezaduty/gemma4-e2b-redteam-activedirectory"

tokenizer = AutoTokenizer.from_pretrained(adapter)
model = AutoModelForCausalLM.from_pretrained(
    base_model, torch_dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(model, adapter)

messages = [
    {"role": "system", "content": [{"type": "text", "text": "You are an expert in Active Directory security and red team operations. You provide deep answers on AD attack paths, lateral movement, credential theft, and defensive hardening."}]},
    {"role": "user",   "content": [{"type": "text", "text": "Your question here"}]},
]
inputs = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
output = model.generate(inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))

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System Prompt

You are an expert in Active Directory security and red team operations. You provide deep answers on AD attack paths, lateral movement, credential theft, and defensive hardening.

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See Also

• General cybersecurity model — full 646-example dataset
• Docker & Container Security
• Kubernetes Security
• AI & LLM Security
• All rezaduty models