How to Use AI Models Like GPT-5.5 for Security Vulnerability Assessment: A Step-by-Step Guide

Introduction

Security vulnerability detection is a critical part of software development, and AI models are increasingly stepping into this role. Recent evaluations by the UK's AI Security Institute have shown that OpenAI's GPT-5.5 is now on par with specialized models like Anthropic's Claude Mythos when it comes to finding security flaws. Even more interesting, smaller and cheaper models can achieve similar results—provided you spend extra time crafting the right prompts. This guide walks you through the process of using AI models like GPT-5.5 to assess your code for vulnerabilities, from setup to analysis. Whether you're a security engineer or a curious developer, these steps will help you integrate AI into your security workflow.

What You Need

• Access to an AI model – OpenAI's GPT-5.5 (generally available) or a comparable model like Claude Mythos. For a lower-cost alternative, consider GPT-4o-mini or other small models.
• API credentials – An API key from the model provider (e.g., OpenAI, Anthropic).
• Sample codebase – A small set of code snippets (e.g., Python, JavaScript) with known vulnerabilities for testing.
• Prompt engineering environment – A text editor or a script that can send API requests and parse responses.
• Baseline references – The official evaluation of Claude Mythos and the analysis of a smaller model for comparison.
• Knowledge of common vulnerabilities – Familiarity with OWASP Top 10 or CWE categories.

Step-by-Step Guide

Step 1: Choose Your AI Model

Your first decision is which model to use. The UK AI Security Institute found GPT-5.5 matches Claude Mythos in vulnerability detection. Mythos is a specialized security model, while GPT-5.5 is a general-purpose model. If budget is a concern, choose a smaller, cheaper model (e.g., GPT-4o-mini). However, be aware that smaller models require more work on your part: they need detailed scaffolding in your prompts to stay focused. For this guide, we’ll assume you start with GPT-5.5, then replicate the process with a smaller model to see the difference.

Step 2: Set Up Your Environment

Create a Python script or use a tool like Postman to interact with the model’s API. Install the required library (e.g., openai for GPT-5.5). Store your API key securely as an environment variable. Write a simple function that sends a prompt and returns the model’s response. For example:

import openai
openai.api_key = os.getenv('OPENAI_API_KEY')
response = openai.ChatCompletion.create(
    model='gpt-5.5',
    messages=[{'role': 'user', 'content': 'Your prompt here'}]
)
print(response.choices[0].message.content)

Test the connection with a trivial query (e.g., “Say hello”).

Step 3: Define the Vulnerability Scope

Before scanning, decide what types of vulnerabilities you want to find. Examples: SQL injection, cross-site scripting (XSS), insecure deserialization, buffer overflows. Narrowing the scope improves accuracy. Write a short description for each type – you’ll include these in your prompts. For a comprehensive scan, you can cycle through multiple vulnerability types.

Step 4: Craft Your Prompts

This is the most critical step, especially for smaller models. A good prompt includes:

• Context: “You are a security expert analyzing code for vulnerabilities.”
• Code snippet: Paste the target code.
• Instructions: “List any SQL injection vulnerabilities in this code. Explain why they are problematic and suggest a fix.”
• Limitations: “Only report vulnerabilities from the OWASP Top 10. Ignore logical errors.”

For GPT-5.5, you can use a simple prompt; for smaller models, add more scaffolding: break the task into sub-steps (e.g., “First, parse the input. Second, identify untrusted data. Third, check if it reaches a query.”). The UK Institute’s analysis of the smaller model shows that extra scaffolding makes it just as effective as the larger model.

Step 5: Run the Initial Scan

Send your first code snippet through the model. Record the response. Pay attention to both false positives and missed vulnerabilities. Do this for at least 5–10 snippets to get a baseline. Keep a log of the model’s output for each snippet. If using GPT-5.5, compare its findings with a manual review or a known vulnerability list.

Step 6: Evaluate Against the Mythos Baseline

Now compare your results with the UK AI Security Institute’s evaluation of Claude Mythos. Did GPT-5.5 catch the same vulnerabilities? Were there any differences? Note the number of true positives, false positives, and missed items. This step validates whether your model is performing at the same level as Mythos.

Step 7: Replicate with a Smaller, Cheaper Model

Switch to a cheaper model (e.g., GPT-4o-mini). You’ll need to increase scaffolding in your prompts – more explicit instructions, breaking down tasks, and providing examples. Test the same code snippets. The UK Institute’s analysis of a smaller model shows that with proper scaffolding, it can be just as good. Log the results and compare to the GPT-5.5 and Mythos baselines.

Step 8: Refine Your Process

Based on the comparisons, tweak your prompts. If a model misses certain vulnerability classes, add more examples or stricter definitions. If it hallucinates, add constraints like “If unsure, state ‘No vulnerability found’.” Iterate until you achieve consistent results. You may also want to combine models: use GPT-5.5 for initial broad scanning, then a smaller model for targeted checks.

Step 9: Document and Share Findings

Create a report that includes the model used, prompt versions, code samples, and results. This documentation helps your team reproduce the process and improves future scans. Note any cost differences – e.g., GPT-5.5 might cost $X per scan, while the smaller model costs $Y but requires extra manual effort. This trade-off is key for decision-making.

Tips for Success

• Start small: Test on a toy codebase before going production. This builds confidence without risking real systems.
• Use role-playing: Set the model’s persona to “senior security engineer” to get more focused output.
• Leverage few-shot prompts: Provide one or two examples of vulnerabilities you want found.
• Beware of token limits: Long code snippets may need splitting. GPT-5.5 has a large context window, but smaller models may truncate.
• Combine with static analysis tools: AI is not a replacement for SAST tools; use both for best coverage.
• Monitor for model changes: AI services update frequently. Re-evaluate baselines after provider updates.
• Cost vs. effort: A cheaper model with more human prompt engineering can be cost-effective if you have the time. A premium model saves time but costs more.

By following these steps, you can harness the power of AI models like GPT-5.5 to strengthen your security posture. The key is to understand the trade-offs and invest in prompt engineering, especially when using smaller models. The UK AI Security Institute’s findings confirm that with the right approach, both high-end and budget-friendly AI can significantly aid vulnerability discovery.