Comparing AI Models for Security Vulnerability Detection: A Practical Guide

Overview

Security vulnerability detection is a critical task in software development, and large language models (LLMs) are increasingly used to assist in finding flaws. Recent evaluations by the UK's AI Security Institute have shown that OpenAI's GPT-5.5 model performs comparably to Claude Mythos in this domain. Importantly, GPT-5.5 is generally available, making it accessible to developers and security teams. This guide will walk you through the process of using LLMs such as GPT-5.5 and Mythos for vulnerability detection, based on the Institute’s findings. It also covers using a smaller, cheaper model that, with additional scaffolding, achieves similar results. By following these steps, you can evaluate AI models for your own security workflows.

Comparing AI Models for Security Vulnerability Detection: A Practical Guide — Source: www.schneier.com

Prerequisites

Before you begin, ensure you have the following:

Access to GPT-5.5 – OpenAI's model is generally available via API. Obtain API credentials from OpenAI.
Understanding of Claude Mythos – Familiarize yourself with Anthropic’s model, which serves as a benchmark. A detailed evaluation is available from the AI Security Institute (see section below).
A smaller, cheaper model – The Institute also analyzed a model requiring more scaffolding. For this guide, we'll use GPT-4o-mini as an example (or any similar cost-effective alternative).
Code samples – Prepare a set of code snippets with known vulnerabilities (e.g., SQL injection, XSS, buffer overflows). Use open-source datasets like OWASP Benchmark.
Basic programming environment – Python 3.8+ installed, along with the requests library for API calls.

Step-by-Step Instructions

1. Set Up Your Environment

Create a Python script to interact with the LLM APIs. Example for GPT-5.5:

import requests

GPT35_API_URL = "https://api.openai.com/v1/chat/completions"
def query_gpt35(prompt, api_key):
    headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
    data = {
        "model": "gpt-5.5",
        "messages": [{"role": "user", "content": prompt}],
        "temperature": 0.2
    }
    response = requests.post(GPT35_API_URL, json=data, headers=headers)
    return response.json()['choices'][0]['message']['content']

Similarly, set up for Mythos and the smaller model. Remember to store API keys securely.

2. Prepare Code Samples

Select 10–20 code snippets from OWASP Benchmark. Ensure each snippet has a ground truth label indicating presence or absence of a vulnerability. Format each snippet as a string to pass in prompts.

3. Prompt GPT-5.5 for Vulnerability Detection

Create a consistent prompt template. For example:

"You are a security expert. Analyze the following code and list any security vulnerabilities. Provide the line number, type, and a brief explanation. If none, say 'No vulnerabilities found'.\n\nCode:\n" + snippet

Iterate through all snippets and collect responses. Record true positives, false positives, true negatives, false negatives.

4. Prompt Claude Mythos for Comparison

Use the same prompt structure with Claude Mythos. The AI Security Institute’s evaluation of Mythos (more details) provides a baseline. Run all snippets and store results.

5. Compare Results

Calculate precision, recall, and F1-score for both models. In the Institute’s findings, GPT-5.5 achieved scores comparable to Mythos, often within a few percentage points. Create a comparison table:

Model	Precision	Recall	F1
GPT-5.5	0.87	0.83	0.85
Mythos	0.88	0.82	0.85

Note: These are illustrative numbers; real results may vary.

6. Using a Smaller, Cheaper Model with Scaffolding

The AI Security Institute also analyzed a smaller model (e.g., GPT-4o-mini) that requires more scaffolding. Scaffolding involves breaking the task into subtasks: identify potential risks, then ask the model to explain each risk, and finally aggregate. Example:

Step A: Prompt the model to list all lines that might contain vulnerabilities.
Step B: For each line, ask: "Is there a vulnerability? Explain."
Step C: Compare answers to decide final output.

This process increases accuracy but requires more manual effort. Remarkably, with proper scaffolding, the smaller model performed just as well as GPT-5.5 and Mythos.

Common Mistakes

Overconfidence: Do not trust model outputs blindly. Always validate findings with static analysis tools or manual review.
Lack of context: LLMs need full context. Provide surrounding code, not just one line.
Ignoring false positives: Models may flag benign patterns. Use a second pass to filter.
Inconsistent prompts: Varying prompt wording can skew results. Standardize across models.
Neglecting scaffolding: For cheaper models, skipping scaffolding leads to poor performance.
Not updating models: AI models evolve. Re-evaluate periodically.

Summary

This guide showed how to replicate the UK AI Security Institute’s evaluation of GPT-5.5 and Claude Mythos for vulnerability detection. You learned to set up API calls, prepare test cases, prompt models, and compare metrics. Additionally, you explored using a smaller model with scaffolding to achieve similar results. By avoiding common pitfalls, you can integrate AI-powered vulnerability scanning into your development cycle effectively.

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