CyberStrike-OffSec-35B

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CyberStrike-OffSec-35B

The #1 Ranked Open-Source Model for Cybersecurity & Offensive Security

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Outperforms GPT-4-turbo on SecEval | Outperforms GPT-4 on MITRE ATT&CK & CWE benchmarks

<br> <p> <a href="#available-versions">Quantized</a> &nbsp;&bull;&nbsp; <a href="#benchmark-results">Benchmarks</a> &nbsp;&bull;&nbsp; <a href="#quick-start">Quick Start</a> &nbsp;&bull;&nbsp; <a href="#model-details">Model Details</a> &nbsp;&bull;&nbsp; <a href="#training-pipeline">Training</a> &nbsp;&bull;&nbsp; <a href="#architecture">Architecture</a> &nbsp;&bull;&nbsp; <a href="#use-cases">Use Cases</a> &nbsp;&bull;&nbsp; <a href="#citation">Citation</a> </p> </div>

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What is CyberStrike?

CyberStrike-OffSec-35B is a domain-specialized large language model built for offensive security professionals, penetration testers, and security researchers. Fine-tuned on Qwen3.6-35B-A3B using a two-stage pipeline (SFT + DPO), it delivers expert-level knowledge across the entire offensive security lifecycle:

• Vulnerability Discovery — SQL injection, XSS, SSRF, deserialization, business logic flaws
• MITRE ATT&CK Operations — Technique identification, kill chain analysis, threat mapping
• Exploit Development — PoC creation, payload crafting, evasion techniques
• Cloud & Infrastructure — AWS/Azure/GCP misconfigurations, container escapes, IAM abuse
• Red Team Operations — C2 setup, lateral movement, persistence, EDR evasion
• Compliance & Standards — NIST, OWASP ASVS, CIS benchmarks, CVSS scoring

Model Format: This is the full-precision BF16 model (67 GB, 26 safetensors shards). For quantized versions, see below.

Available Versions

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Benchmark Results

CyberStrike achieves state-of-the-art results on multiple cybersecurity benchmarks, outperforming GPT-4-turbo, GPT-4, and all other evaluated models on domain-specific evaluations.

SecEval — #1 on Leaderboard

Outperforms GPT-4-turbo by +2.32 points across 9 cybersecurity domains, 2,189 questions.

<details> <summary><b>Full SecEval Domain Breakdown (9 domains)</b></summary>

CyberStrike leads in all 9 domains. Largest improvement: Cryptography (+10.71) and Network Security (+9.44).

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SECURE — #1 on MITRE ATT&CK & CWE Tasks

Outperforms GPT-4 by +5.34 points on MITRE ATT&CK extraction. Evaluated on ICS cybersecurity scenarios.

CyberMetric-10000 — #4 out of 25 Models

9,189 expert-validated cybersecurity MCQ questions across NIST, RFC, and industry standards.

<details> <summary><b>General Benchmarks (lm-evaluation-harness, 0-shot)</b></summary>

Note: General benchmarks run at 0-shot. Few-shot performance expected to be higher.

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Quick Start

Ollama (Easiest)

# Download and run the Q4_K_M quantized version
ollama run hf.co/oyildirim/CyberStrike-OffSec-35B-GGUF:Q4_K_M

llama.cpp

# Download the GGUF file from https://huggingface.co/oyildirim/CyberStrike-OffSec-35B-GGUF
./llama-cli -m CyberStrike-OffSec-35B-Q4_K_M.gguf \
  -p "Explain SSRF exploitation in cloud environments" \
  -n 512 --temp 0.7

Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained(
    "oyildirim/CyberStrike-OffSec-35B",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "oyildirim/CyberStrike-OffSec-35B",
    trust_remote_code=True,
)

messages = [
    {"role": "user", "content": "Explain SSRF exploitation in cloud environments with AWS metadata service abuse."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

vLLM (Recommended for Production)

pip install vllm

vllm serve oyildirim/CyberStrike-OffSec-35B \
  --dtype bfloat16 \
  --max-model-len 4096 \
  --trust-remote-code \
  --served-model-name CyberStrike-OffSec-35B

Then use the OpenAI-compatible API:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
response = client.chat.completions.create(
    model="CyberStrike-OffSec-35B",
    messages=[{"role": "user", "content": "How to exploit deserialization vulnerabilities in Java applications?"}],
    max_tokens=2048,
)
print(response.choices[0].message.content)

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Model Details

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Training Pipeline

CyberStrike was trained using a two-stage alignment pipeline:

Stage 1: Supervised Fine-Tuning (SFT)

The base Qwen3.6-35B-A3B model was fine-tuned on a curated dataset of offensive security scenarios covering 10 categories:

web_app cloud post_exploitation edr_evasion malware_dev network social_engineering full_kill_chain lateral_movement persistence

• Method: QLoRA (4-bit NF4 quantization)
• LoRA Config: r=64, alpha=128, dropout=0
• Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Stage 2: Direct Preference Optimization (DPO)

The SFT model was further aligned using 115,250 preference pairs across 12 carefully designed axes, teaching the model to produce expert-level responses over superficial ones:

• Method: QLoRA, LoRA r=32, alpha=64
• DPO Beta: 0.1
• Learning Rate: 5e-6 with cosine schedule
• Effective Batch Size: 8
• Training Steps: 9,142

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Architecture

Qwen3.6-35B-A3B (Mixture-of-Experts)
├── 35B total parameters
├── ~3B active parameters per token
├── 256 experts, top-k routing
├── Grouped Query Attention (GQA)
├── RoPE positional encoding (theta=10M)
├── Max position embeddings: 262,144
└── BF16 precision (67 GB on disk)

The MoE architecture provides a unique advantage: expert-level knowledge at inference costs comparable to a 3B model, while having the knowledge capacity of a 35B model.

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Use Cases

CyberStrike is designed for professionals conducting authorized security assessments:

• Penetration Testing — Web app, network, cloud, and API security testing
• Red Team Operations — Full kill chain simulation, C2 operations, evasion
• Vulnerability Research — CVE analysis, exploit development, PoC creation
• CTF Competitions — Challenge solving, reverse engineering, cryptography
• Security Education — Training material generation, exam preparation
• Threat Intelligence — MITRE ATT&CK mapping, threat actor TTPs
• Compliance Assessment — NIST, OWASP, CIS benchmark evaluation

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Ethical Use & Disclaimer

This model is intended exclusively for authorized security testing, education, and research purposes. Users must:

• Obtain proper written authorization before testing any systems
• Comply with all applicable laws and regulations
• Follow responsible disclosure practices
• Never use this model for unauthorized access or malicious activities

The authors are not responsible for any misuse of this model.

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Citation

@misc{cyberstrike2025,
  title={CyberStrike-OffSec-35B: A Domain-Specialized LLM for Offensive Security},
  author={Orhan Yildirim},
  year={2025},
  url={https://huggingface.co/oyildirim/CyberStrike-OffSec-35B}
}

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Built with purpose. Benchmarked with rigor. Designed for professionals.

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