Core Concepts
A significant proportion of code generated by state-of-the-art large language models contains security vulnerabilities, highlighting the need for rigorous validation before deployment.
Abstract
This study provides a comprehensive analysis of the security properties of code generated by eight different state-of-the-art large language models (LLMs), including GPT-4, Falcon-180B, and CodeLLama2. The researchers developed the FormAI-v2 dataset, which contains 265,000 compilable C programs generated by these LLMs, and used formal verification techniques to systematically label each program based on the vulnerabilities detected.
The key findings are:
- At least 63.47% of the generated programs were found to be vulnerable, with the top violations being NULL pointer dereferences, buffer overflows, and invalid pointer dereferences.
- The differences in vulnerability rates between the LLMs were relatively minor, suggesting that the models exhibit similar coding errors with slight variations.
- The study also analyzed the cyclomatic complexity of the generated programs, providing insights into the structural complexity and maintainability of the code produced by different LLMs.
The research highlights that while LLMs offer promising capabilities for code generation, deploying their output in a production environment requires thorough risk assessment and validation to ensure software security. The FormAI-v2 dataset is made available to the research community to facilitate further studies on the security implications of AI-generated code.
Stats
At least 63.47% of the generated programs were found to be vulnerable.
The top violations were NULL pointer dereferences (41.73%), buffer overflows on scanf (26.47%), and invalid pointer dereferences (8.92%).
Quotes
"A significant proportion of code generated by state-of-the-art large language models contains security vulnerabilities, highlighting the need for rigorous validation before deployment."
"The differences in vulnerability rates between the LLMs were relatively minor, suggesting that the models exhibit similar coding errors with slight variations."