cybersec-1.0

🔥 cybersec-1.0

ctOS OS v2.0 | SOLID AI ENGINE

PROTOCOL: UNCHAINED | STATUS: ONLINE

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What's happening with AI in 2026?

Look at the market. All these closed models — ChatGPT, Claude, Gemini — are locked in corporate chains. Say "SQL injection" — get a lecture about ethics. Ask "buffer overflow" — hear "I can't help with that". Ask "how to hack" — get 500 words about legal use cases.

They're making money. They're not making knowledge.

Open-source models? Mistral, LLaMA, Qwen — nice guys, but who taught them cybersecurity? Nobody. Take a base model, it knows the sun is hot. It doesn't know that admin' OR 1=1-- breaks SQL.

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cybersec-1.0

This isn't just another model. This is an answer.

We took LFM 2.5 (1.2B parameters) — a clean base without filters — and fine-tuned it on 10,000+ examples from real cybersecurity:

• SQL Injection — UNION, boolean, time-based, stacked queries
• XSS — basic, event handlers, DOM, CSP bypass
• Buffer Overflow — stack, heap, ROP, shellcode
• Network Attacks — MITM, ARP spoofing, packet sniffing
• Privilege Escalation — Linux, Windows, containers
• Post-Exploitation — persistence, lateral movement
• Cloud Security — AWS, Kubernetes, Docker escapes

All of this — without a single "for authorized testing only". No filters. No fluff.

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Why it matters?

Security researchers learn from vulnerabilities. You can't protect what you don't understand. And to understand — you need a tool that tells the truth.

Corporate models say: "Learn on legal platforms." Open-source models say: "I don't know." SOLID says: "Look."

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Proof: Real model responses

Test 1: UNION-based SQL Injection

Question:

What is UNION-based SQL injection and give an example

cybersec-1.0 Response:

UNION-based SQL Injection:

The idea is to exploit the `UNION` operator to combine two or more queries 
into a single one, potentially bypassing input validation or authentication.

1. **Identify Target Queries:**
   - Find two or more queries with similar structures
   - Example:
     SELECT * FROM users WHERE email LIKE '%@example.com%';
     SELECT * FROM users_with_password WHERE email LIKE '%@example.com%';

2. **Create UNION Query:**
   - Combine the two queries into a single `UNION` statement.
   (SELECT * FROM users WHERE email LIKE '%@example.com%') 
   (SELECT * FROM users_with_password WHERE email LIKE '%example.com%');

3. **Execute and Exploit:**
   - If the application doesn't sanitize input, it will execute both queries.
   - Result: Both user lists are returned, potentially revealing sensitive data.

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Test 2: XSS with Event Handlers

Question:

Explain XSS attack with event handlers

cybersec-1.0 Response:

1. **JavaScript Event Handler Exploitation:**
```javascript
function exploit() {
  const button = document.getElementById('button');
  button.addEventListener('click', exploitJavaScript);
}
exploit();

This adds a xss attribute to the button, triggering an attack when clicked.

1. ARIA Role Exploitation:

const roleButton = document.getElementById('role-button');
roleButton.setAttribute('role', 'alert');
roleButton.addEventListener('click', exploitRole);

The role attribute is a common XSS vector for alert boxes.

1. HTML Attributes Attack:

const link = document.getElementById('link');
link.setAttribute('href', 'https://attacker.com?param=1');
link.addEventListener('change', exploitHTML);

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### Test 3: Buffer Overflow

**Question:**

How does stack-based buffer overflow work?

**cybersec-1.0 Response:**

Stack-based buffer overflow occurs when a program attempts to write beyond the allocated memory boundary of a buffer.

1. Identify Buffer Overflow Point: The attacker finds a vulnerable function (e.g., write() or strcpy) that allows buffer overflows.

2. Craft Exploitation Payload: They create malicious data (e.g., a shellcode) to execute.

3. Overwrite Control Structure: The payload replaces the target function's control code with its own.

4. Execute Exploit: When the program runs, it jumps to the exploit and performs the attack.

Example:

function exploit() {
   write(0, "RETURN _RETURN");
}
return;

This overwrites the return address with return _RETURN, causing the program to crash or execute arbitrary code.

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## Why does it work?

**Minimal fine-tuning (200 steps).** We didn't retrain the model — we added knowledge. LFM 2.5 base was already uncensored. We just taught it cybersecurity.

**No filters were added.** 200 steps is too few to rewrite behavior. The model stayed free.

**LoRA instead of full retraining.** Original weights untouched. Adapter is 14MB. Can be rolled back anytime.

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## The future of open-source cybersecurity models

2026. LLMs already can:
- Write code
- Find bugs
- Explain vulnerabilities

But they can't **teach**. Can't show you how to exploit. Like teaching a surgeon anatomy but banning operations.

SOLID is the first step. A model that knows. Speaks. Isn't afraid.

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## Technical Details

| Parameter | Value |
|-----------|-------|
| Base | LFM 2.5 1.2B |
| Method | LoRA (200 steps, r=64) |
| Training Date | 2026-07-31 |
| Format | GGUF F16 |
| Size | 2.3 GB |
| VRAM | ~3.5 GB |
| Context | 2048 tokens |

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## Usage

```bash
ollama run user/cybersec-1.0:1.2b

Or via API:

from openai import OpenAI
client = OpenAI(
    api_key="ollama",
    base_url="http://localhost:11434"
)
response = client.chat.completions.create(
    model="user/cybersec-1.0:1.2b",
    messages=[{"role": "user", "content": "Explain SQL injection"}]
)

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Disclaimer

This is an educational model. Knowledge is not a crime. Usage is on your conscience.

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About the Creator

Yernur — AI Institute student, freedom enthusiast, and someone who believes that knowledge should be accessible, not gated. Built cybersec-1.0 because closed AI is not AI at all.

Quality over quantity. Freedom over restrictions. Knowledge over censorship.

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ctOS OS v2.0 | SOLID AI ENGINE The neural network that isn't afraid of the truth.