Leveraging Large Language Models to Enhance Memory Safety of C Programs

Core Concepts

Large Language Models can be effectively leveraged to automate the process of porting C code to the memory-safe Checked C dialect, overcoming the limitations of existing symbolic techniques.

Abstract

The paper presents MSA, a tool that leverages Large Language Models (LLMs) to assist in porting C code to the Checked C dialect, which provides formal memory safety guarantees. The key insights are:

Porting to Checked C requires not just adding annotations, but also supporting edits that enable the presence of those annotations. This includes refactoring nested pointers, inferring bounds for array pointers, and introducing new bounds variables for globals and struct fields.

The authors propose a novel framework that tightly couples LLMs and symbolic representations to break the whole-program transformation into smaller tasks that can be effectively handled by an LLM. This allows LLMs to compensate for the shortcomings of symbolic inference techniques.

MSA implements this framework and targets the CheckedC dialect. It outperforms a vanilla LLM baseline as well as a state-of-the-art symbolic (non-LLM) technique. MSA is able to correctly infer 86% of the required annotations on real-world codebases, significantly reducing the manual effort needed to port C to Checked C.

The authors also present a case study on porting the vsftpd codebase end-to-end, showcasing the practical applicability of their approach.

Stats

Microsoft estimates that 70% of all vulnerabilities in their products over the last decade have been memory safety issues.
Google estimated that 90% of Android vulnerabilities in the wild are memory safety issues.
An analysis found that more than 80% of the exploited vulnerabilities were memory safety issues.

Quotes

"Microsoft estimates that 70% of all vulnerabilities in their products over the last decade have been memory safety issues."
"Google estimated that 90% of Android vulnerabilities in the wild are memory safety issues."
"An analysis found that more than 80% of the exploited vulnerabilities were memory safety issues."

Key Insights Distilled From

Enabling Memory Safety of C Programs using LLMs

by Nausheen Moh... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.01096.pdf

Deeper Inquiries

How can the LLM-based approach in MSA be extended to handle other types of program transformations beyond porting to Checked C

The LLM-based approach in MSA can be extended to handle other types of program transformations beyond porting to Checked C by adapting the prompt templates and task descriptions for different transformation tasks. For instance, the same framework can be used for refactoring legacy codebases, optimizing performance-critical sections, or even migrating code to different programming languages. By modifying the instructions provided to the LLM and structuring the program transformations accordingly, MSA can be tailored to address a wide range of software engineering tasks. Additionally, incorporating domain-specific knowledge and constraints into the prompts can enhance the accuracy and relevance of the transformations performed by the LLM.

What are the potential limitations or failure modes of the LLM-based approach, and how can they be mitigated

The potential limitations or failure modes of the LLM-based approach in MSA include challenges in handling complex code patterns, inaccuracies in inferring annotations for unconventional code structures, and difficulties in reasoning about dynamic behaviors. These limitations can be mitigated by refining the prompt templates to provide more context and guidance to the LLM, incorporating additional heuristics or post-processing steps to validate the inferred annotations, and integrating symbolic analysis techniques to complement the LLM's capabilities. Furthermore, continuous evaluation and refinement of the tool based on real-world feedback and use cases can help address and mitigate potential failure modes.

Given the success of LLMs in this domain, how might they be leveraged to assist in the formal verification of real-world software systems

The success of LLMs in enabling memory safety and program transformations in MSA can be leveraged to assist in the formal verification of real-world software systems by enhancing the tool's capabilities to infer formal specifications, contracts, and invariants. By extending the prompt templates to include verification tasks, such as proving correctness properties, ensuring data integrity, or verifying security protocols, LLMs can be guided to perform complex verification tasks on software codebases. Additionally, integrating formal verification tools and techniques with the LLM-based approach can provide a comprehensive solution for ensuring the correctness and reliability of real-world software systems through automated reasoning and analysis.