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rezaduty/gemma4-e2b-privesc-windows

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face
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Gemma 4 E2B — Windows Privilege Escalation Expert

A QLoRA fine-tuned version of Gemma 4 E2B Instruct specialized in windows privilege escalation. Specialized in Windows privilege escalation: service misconfigurations, token impersonation (Potato family), UAC bypass, registry attacks, scheduled tasks, kernel exploits, and credential hunting.

Part of the rezaduty cybersecurity model family.

Expertise

  • —Methodology: WinPEAS, PowerUp, Seatbelt enumeration
  • —Service misconfigurations: unquoted paths, weak ACLs, DLL hijacking
  • —Token impersonation: JuicyPotato, PrintSpoofer, RoguePotato (Potato family)
  • —UAC bypass techniques: fodhelper, eventvwr, DiskCleanup, ICMLuaUtil
  • —Registry privesc: AlwaysInstallElevated, autoruns, winlogon credentials
  • —SeBackupPrivilege, SeRestorePrivilege, SeDebugPrivilege abuse
  • —Kernel exploits and patch-gap exploitation

Model Details

PropertyValue
Base modelgoogle/gemma-4-e2b-it (2B parameters)
Fine-tuning methodQLoRA (rank 16, α 16)
DomainWindows Privilege Escalation
Datasetrezaduty/cybersecurity-qa-v2
LicenseApache 2.0

Usage

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

base_model = "google/gemma-4-e2b-it"
adapter    = "rezaduty/gemma4-e2b-privesc-windows"

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 Windows privilege escalation techniques. Provide deep technical answers on Windows privesc methods, detection strategies, and hardening measures with specific commands, tool names, and CVE references where applicable."}]},
    {"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))

System Prompt

You are an expert in Windows privilege escalation techniques. Provide deep technical answers on Windows privesc methods, detection strategies, and hardening measures with specific commands, tool names, and CVE references where applicable.

See Also