A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification (2305.14752v2)

Abstract: This paper introduces an innovative approach that combines LLMs with Formal Verification strategies for automatic software vulnerability repair. Initially, we employ Bounded Model Checking (BMC) to identify vulnerabilities and extract counterexamples. These counterexamples are supported by mathematical proofs and the stack trace of the vulnerabilities. Using a specially designed prompt, we combine the original source code with the identified vulnerability, including its stack trace and counterexample that specifies the line number and error type. This combined information is then fed into an LLM, which is instructed to attempt to fix the code. The new code is subsequently verified again using BMC to ensure the fix succeeded. We present the ESBMC-AI framework as a proof of concept, leveraging the well-recognized and industry-adopted Efficient SMT-based Context-Bounded Model Checker (ESBMC) and a pre-trained transformer model to detect and fix errors in C programs, particularly in critical software components. We evaluated our approach on 50,000 C programs randomly selected from the FormAI dataset with their respective vulnerability classifications. Our results demonstrate ESBMC-AI's capability to automate the detection and repair of issues such as buffer overflow, arithmetic overflow, and pointer dereference failures with high accuracy. ESBMC-AI is a pioneering initiative, integrating LLMs with BMC techniques, offering potential integration into the continuous integration and deployment (CI/CD) process within the software development lifecycle.

A New Era in Software Security: Towards Self-Healing Software via LLMs and Formal Verification

The paper "A New Era in Software Security: Towards Self-Healing Software via LLMs and Formal Verification" introduces an intriguing integration of LLMs and formal verification methodologies to address software vulnerabilities through an automated repair process. This integration, specifically employing Bounded Model Checking (BMC) and LLMs, presents a novel approach to automated program repair by highlighting the dual capability of detecting and amending software defects.

Methodology Overview

The paper is structured around an innovative framework that utilizes the strengths of both symbolic BMC and LLMs to locate and fix errors in C programs. The process begins with BMC for fault localization, which identifies vulnerabilities or incorrect behaviors in the software and generates a counterexample. This counterexample, alongside the source code, serves as an input to an LLM, in this case, a pre-trained Transformer model (GPT-3.5-turbo). The LLM is tasked with understanding the root causes of these vulnerabilities and generating code patches. Subsequently, these fixes are subjected to another round of verification via BMC to ensure they adhere to the initial safety and security specifications.

Experimental Findings

The empirical evaluation conducted leveraged a dataset of 1000 C code samples, each ranging between 20 to 50 lines of code. The automated repair mechanism exhibited a success rate of up to 80% in addressing vulnerabilities such as buffer overflows and pointer dereference errors. This figure underscores the considerable potential of combining LLMs with formal verification methods to enhance software robustness effectively.

Implications and Future Prospects

This research offers valuable insights into the future of software development. Integrating AI technologies with rigorous formal methods hints at an evolving landscape where software systems can exhibit self-healing capabilities. Such systems would continuously integrate, assess, and rectify vulnerabilities, significantly bolstering the development lifecycle's security protocols.

From a theoretical standpoint, this paper posits an interesting model for leveraging AI in software engineering, particularly in an era where traditional fault localization methods encounter scalability challenges. The juxtaposition of LLMs' understanding and generation capabilities with the precision of BMC offers a hybrid approach that can pave the way for the next generation of software verification and repair tools.

Conclusion

This paper effectively bridges a gap between static analysis and dynamic learning in software security, advocating a paradigm where automation does not merely detect issues but contributes to their solution. As AI continues to advance, the role of LLMs in software development will likely expand, encompassing more sophisticated bug detection and repair tasks. Exploring complex integrations and deploying these systems in diverse real-world scenarios remains a promising avenue for future research, potentially leading to significant advancements in AI-driven cybersecurity.