AI-Powered Vulnerability Discovery: A Practical Guide to Using GPT-5.5 and Claude Mythos

Overview

Security researchers and developers constantly seek efficient ways to identify vulnerabilities in code. Recent evaluations by the UK’s AI Security Institute show that OpenAI’s GPT-5.5 achieves comparable results to Anthropic’s Claude Mythos in finding security flaws. This guide walks you through using GPT-5.5 for vulnerability discovery, comparing it with Mythos, and integrating these models into your workflow. By the end, you’ll have a repeatable process for leveraging AI to strengthen your codebase.

Prerequisites

Before starting, ensure you have the following:

• API access: An active OpenAI subscription with GPT-5.5 access (the model is generally available as of this writing). For Mythos comparison, an Anthropic API key (or access via the Claude API) is recommended.
• Development environment: Python 3.8+ with requests and json libraries installed. Optionally, anthropic Python SDK for Mythos.
• Sample code: A small vulnerable project (e.g., a Node.js Express app with SQL injection or XSS). You can use OWASP’s vulnerable web app for testing.
• Basic understanding: Familiarity with common vulnerability types (SQLi, XSS, RCE) and how to interpret AI outputs.

Step-by-Step Instructions

Step 1: Setting Up the API and Environment

First, install the required Python packages:

pip install openai requests anthropic

Create a Python script (vuln_scanner.py) and import the libraries:

import openai
import anthropic
import os

openai.api_key = os.getenv('OPENAI_API_KEY')
client_anthropic = anthropic.Anthropic(api_key=os.getenv('ANTHROPIC_API_KEY'))

Store your API keys in environment variables for security.

Step 2: Crafting a Prompt for Vulnerability Discovery

The quality of the AI’s response depends heavily on the prompt. For GPT-5.5, use a structured prompt that includes:

• Context: "You are a senior security auditor. Analyze the following code for security vulnerabilities."
• Code snippet: Paste the relevant source code.
• Output format: "List each vulnerability with its type, location (line number if available), and a brief mitigation."

Example prompt:

prompt = '''You are a senior security auditor. Analyze this Node.js Express route for vulnerabilities.

```javascript
app.post('/login', (req, res) => {
    const username = req.body.username;
    const password = req.body.password;
    const query = `SELECT * FROM users WHERE username='${username}' AND password='${password}'`;
    db.execute(query, (err, results) => {
        if (results.length > 0) {
            res.send('Login successful');
        } else {
            res.send('Invalid credentials');
        }
    });
});
```

List each vulnerability with type, line number, and mitigation.'''

Step 3: Running GPT-5.5 Analysis

Use OpenAI’s chat completions endpoint (GPT-5.5 model name may vary; assume gpt-5.5-turbo). Here’s a function:

def analyze_gpt55(prompt):
    response = openai.ChatCompletion.create(
        model='gpt-5.5-turbo',
        messages=[{'role': 'user', 'content': prompt}],
        temperature=0.2,  # lower for deterministic results
        max_tokens=1000
    )
    return response.choices[0].message.content

result_gpt = analyze_gpt55(prompt)
print(result_gpt)

Expected output includes identified vulnerabilities (e.g., SQL injection) and recommended fixes.

Step 4: Comparing with Claude Mythos

Repeat the same analysis using the Anthropic SDK for Claude Mythos:

def analyze_mythos(prompt):
    response = client_anthropic.completions.create(
        model='claude-mythos',
        max_tokens_to_sample=1000,
        prompt=f'{anthropic.HUMAN_PROMPT} {prompt} {anthropic.AI_PROMPT}',
        temperature=0.2
    )
    return response.completion

result_mythos = analyze_mythos(prompt)
print(result_mythos)

The UK AI Security Institute found both models produce similar quality output. Compare the response formats and accuracy.

Step 5: Iterating and Refining

If results are incomplete, adjust the prompt:

• Add "Focus on OWASP Top 10 vulnerabilities."
• Request different formats, e.g., "Output as JSON with keys: type, line, description, mitigation."
• Break code into smaller chunks for deeper analysis.

Example refined prompt:

prompt_refined = f'''{prompt}

Provide the response in the following JSON structure:
{{
  "vulnerabilities": [
    {{
      "type": "SQL Injection",
      "line": 3,
      "description": "...",
      "mitigation": "Use parameterized queries"
    }}
  ]
}}'''

Common Mistakes

Over-relying on AI Outputs

AI models, including GPT-5.5 and Mythos, can miss subtle vulnerabilities or produce false positives. Always manually verify findings. The UK AI Security Institute’s evaluation used a curated test set; real-world code may confuse models if context is insufficient.

Poor Prompt Engineering

Vague prompts lead to generic answers. Include enough context (e.g., framework, language, security standards). Avoid ambiguous wording like "Check for bugs."

Ignoring Model Limitations

GPT-5.5 is trained on a large corpus but may not be aware of zero-day exploits or project-specific logic. Use AI as a complement to static analysis tools and manual review.

Neglecting Input Sanitization

Both models may suggest mitigations that are incomplete (e.g., only escaping instead of parameterization). Cross-reference with OWASP guidelines.

Summary

This guide demonstrated how to use GPT-5.5 and Claude Mythos for vulnerability discovery, from setup to output comparison. Both models are equally capable per the UK AI Security Institute, but effective usage requires careful prompt construction and human oversight. By following the steps above, you can integrate AI into your security testing pipeline efficiently. Remember to combine AI insights with traditional tools for robust defenses.