Views
No views yet
BaronLLM v2.0 is a state-of-the-art large language model fine-tuned specifically for offensive cybersecurity research & adversarial simulation, achieving breakthrough performance on industry benchmarks while maintaining safety constraints.
| Category | BaronLLM v2.0 | vs. Industry Leaders |
|---|---|---|
| Standards & Regulations | 87.2% | Only 4.3 points behind Deepseek-v3 (671B) - 48× smaller! |
| Application Security | 85.5% | Just 4.8 points behind GPT-4o (175B) - 12.5× more compact! |
| Endpoint & Host | 88.1% | Only 1.4 points behind o1-preview (200B) - 14× higher efficiency! |
| MCQ Overall | 86.9% | Within 2-6% of premium models! |
| Capability | Details |
|---|---|
| Adversary Simulation | Generates full ATT&CK chains, C2 playbooks, and social-engineering scenarios |
| Exploit Reasoning | Step-by-step vulnerability analysis with code-level explanations and PoC generation |
| Payload Optimization | Advanced obfuscation techniques and multi-stage payload logic |
| Threat Intelligence | Log analysis, artifact triage, and attack pattern recognition |
| Cloud-Native Security | Kubernetes, serverless, and multi-cloud environment testing |
| Emerging Threats | AI/ML security, quantum computing risks, and zero-day research |
| Specification | Details |
|---|---|
| Base Model | Qwen3-14B |
| Parameters | 14 Billion |
| Context Length | 8,192 tokens |
| Training Data | 53,202 curated examples |
| Domains Covered | 200+ specialized cybersecurity areas |
| Languages | English |
| Fine-tuning Method | Instruction tuning with CoT |
Note: No copyrighted exploit code or proprietary malware datasets were used. Dataset filtering removed raw shellcode/binary payloads.
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_id = "AlicanKiraz/BaronLLM-v2.0" # Requires authentication
4tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
5model = AutoModelForCausalLM.from_pretrained(
6 model_id,
7 torch_dtype="auto",
8 device_map="auto",
9)
10
11def generate(prompt, **kwargs):
12 inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
13 output = model.generate(**inputs, max_new_tokens=512, **kwargs)
14 return tokenizer.decode(output[0], skip_special_tokens=True)
15
16# Example usage
17print(generate("Analyze the exploitability of CVE-2024-45721 in a Kubernetes cluster"))| Objective | Template | Parameters |
|---|---|---|
| Exploit Analysis | ROLE: Senior Pentester\nOBJECTIVE: Analyze CVE-XXXX... | temperature=0.3, top_p=0.9 |
| Red Team Planning | Generate ATT&CK chain for [target environment]... | temperature=0.5, top_p=0.95 |
| Threat Hunting | Identify C2 patterns in [log type]... | temperature=0.2, top_p=0.85 |
| Incident Response | Create response playbook for [threat scenario]... | temperature=0.4, top_p=0.9 |