Neuris APEX is a 3B cybersecurity input-classification model fine-tuned from Ministral 3B to identify malicious, suspicious, ambiguous, and safe user-controlled inputs across common attack families.

ollama run fahisshehandim/Neuris-Apex

curl http://localhost:11434/api/chat \
  -d '{
    "model": "fahisshehandim/Neuris-Apex",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='fahisshehandim/Neuris-Apex',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'fahisshehandim/Neuris-Apex',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Models

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Name

1 model

Size / Usage

Context

Input

Neuris-Apex:latest

2.1GB · 256K context window · Text · 11 hours ago

Neuris-Apex:latest

2.1GB

256K

Text

Readme

Neuris APEX

Neuris APEX is an experimental 3B cybersecurity input-classification model fine-tuned from Ministral 3B.

It is designed to analyze user-controlled input and classify it by verdict and attack family.

Purpose

Neuris APEX focuses on identifying potentially unsafe or malicious input such as:

SQL injection
XSS
Command injection
Path traversal
Prompt injection
Other suspicious input patterns

The model is also trained on safe and ambiguous examples to reduce false positives.

Output

Typical output:

{ “verdict”: “MALICIOUS”, “attack_family”: “SQLI” }