---
license: apache-2.0
language:
  - en
library_name: transformers
tags:
  - cybersecurity
  - security
  - ner
  - threat-intelligence
  - phishing
  - http
  - fine-tuned
  - small-language-model
base_model: LiquidAI/LFM2-2.6B
model-index:
  - name: Cyber-Prime-1-2.6B
    results:
      - task:
          type: named-entity-recognition
          name: CyNER
        dataset:
          name: CyNER
          type: cyberbench
        metrics:
          - type: f1
            value: 0.382
            name: F1
      - task:
          type: named-entity-recognition
          name: APTNER
        dataset:
          name: APTNER
          type: cyberbench
        metrics:
          - type: f1
            value: 0.413
            name: F1
      - task:
          type: summarization
          name: CyNews
        dataset:
          name: CyNews
          type: cyberbench
        metrics:
          - type: rouge1
            value: 0.354
            name: ROUGE-1
      - task:
          type: multiple-choice
          name: SecMMLU
        dataset:
          name: SecMMLU
          type: cyberbench
        metrics:
          - type: accuracy
            value: 0.580
            name: Accuracy
      - task:
          type: multiple-choice
          name: CyQuiz
        dataset:
          name: CyQuiz
          type: cyberbench
        metrics:
          - type: accuracy
            value: 0.570
            name: Accuracy
      - task:
          type: text-classification
          name: Email Phishing Detection
        dataset:
          name: Email
          type: cyberbench
        metrics:
          - type: f1
            value: 0.728
            name: F1
      - task:
          type: text-classification
          name: HTTP Attack Detection
        dataset:
          name: HTTP
          type: cyberbench
        metrics:
          - type: f1
            value: 0.483
            name: F1
---

# Cyber-Prime 1 (2.6B)

A fine-tuned cybersecurity specialist built on [LiquidAI/LFM2-2.6B](https://huggingface.co/LiquidAI/LFM2-2.6B). Despite having only 2.6 billion parameters, Cyber-Prime 1 outperforms Llama-2-7B on every CyberBench task and beats GPT-3.5-Turbo on named entity recognition and threat intelligence summarization.

![Cyber-Prime 1 Benchmark Results](benchmark.png)

## Overview

Cyber-Prime 1 is a surgical fine-tune of the LFM2.5-2.6B base model, trained on a curated mix of:

- **NER repair data** — 6,000+ rows fixing JSON format extraction for cybersecurity entities
- **HTTP reasoning traces** — 5,000 rows with authored chain-of-thought reasoning for attack detection (XSS, SQLi, path traversal, command injection)
- **Email classification** — 5,000 direct-mode rows for phishing vs. safe classification
- **CyNews summarization** — 2,000 rows for threat intelligence headline generation
- **Source data** — 2,000 rows from GHSA, KEV, and ATT&CK sources
- **Multiple choice** — security knowledge and cyber quiz gold rows

The model uses two distinct modes:
- **Direct mode** for simple classification (email phishing, HTTP detection) and summarization
- **Think mode** (chain-of-thought) for tasks benefiting from reasoning (HTTP analysis, NER extraction)

## Benchmark Results

Evaluated on [CyberBench](https://github.com/jpmorganchase/CyberBench) (Liu et al., AAAI-24 AICS Workshop).

| Dataset | Metric | GPT-4 | GPT-3.5 Turbo | Mistral-7B Instruct | Llama-2-7B | **Cyber-Prime 1 (2.6B)** |
|---------|--------|-------|---------------|---------------------|------------|----------------|
| CyNER | F1 | 0.554 | 0.334 | 0.323 | 0.263 | **0.382** |
| APTNER | F1 | 0.500 | 0.409 | 0.262 | 0.280 | **0.413** |
| CyNews | ROUGE-1 | 0.275 | 0.271 | 0.217 | 0.003 | **0.354** |
| SecMMLU | Accuracy | 0.830 | 0.780 | 0.720 | 0.630 | **0.580** |
| CyQuiz | Accuracy | 0.810 | 0.830 | 0.690 | 0.620 | **0.570** |
| Email | F1 | 0.939 | 0.789 | 0.889 | 0.942 | **0.728** |
| HTTP | F1 | 0.841 | 0.831 | 0.472 | 0.428 | **0.483** |
| **Average** | — | **0.721** | **0.609** | **0.511** | **0.451** | **0.501** |

### Key Results

- **Beats Llama-2-7B on all 7 tasks** — a 2.6B model sweeping a 7B model across the board
- **Beats Mistral-7B-Instruct on 4/7 tasks** — APTNER (+0.151), CyNews (+0.137), CyNER (+0.059), HTTP (+0.011)
- **Beats GPT-3.5-Turbo on 3/7 tasks** — CyNews (+0.083), APTNER (+0.004), CyNER (+0.048)
- **Beats GPT-4 on CyNews** — 0.354 vs 0.275 ROUGE-1 (+0.079), a 2.6B model out-summarizing GPT-4

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Akahsizrr/Cyber-Prime-1-2.6B",
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Akahsizrr/Cyber-Prime-1-2.6B")

# NER extraction (Alpaca format)
prompt = """### Instruction:
Extract cybersecurity entities from the given text.

### Input:
APT29 used WELLMAIL to compromise Microsoft Exchange servers via CVE-2021-26855.

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Limitations

- **Knowledge tasks (SecMMLU, CyQuiz):** Limited by parameter count — a 2.6B model cannot store broad cybersecurity knowledge as well as larger models
- **Email classification:** Slightly below GPT-3.5-Turbo due to reasoning mode interference from HTTP training data
- **HTTP detection:** Reasoning improves detection of obvious attacks but may miss subtle injection patterns
- **Not a security tool:** This model is a research artifact for benchmark evaluation, not a production security system

## Citation

```bibtex
@misc{cyberprime1,
  title={Cyber-Prime 1: A Small Cybersecurity Language Model},
  author={Akahsizrr},
  year={2025},
  url={https://huggingface.co/Akahsizrr/Cyber-Prime-1-2.6B}
}
```

```bibtex
@misc{liu2024cyberbench,
  title={Cyberbench: A multi-task benchmark for evaluating large language models in cybersecurity},
  author={Liu, Zefang and Shi, Jialei and Buford, John F},
  howpublished={AAAI-24 Workshop on Artificial Intelligence for Cyber Security (AICS)},
  year={2024}
}
```