使用 Gemini 1.5 Pro 进行漏洞检测实验_AI阅读总结 — 包阅AI

Unpatched software vulnerabilities can have serious consequences. At Google Cloud, we want developers to reduce the risks they face by focusing on developing code that is secure by design and secure by default. While secure development can be time-consuming, generative AI can be used responsibly to help make that development process faster.

At Google, we’ve been exploring how gen AI tools can help secure code, which can pave the way for more secure and robust software applications. We demonstrated recently how we were able to reverse engineer and analyze the decompiled code of the WannaCry malware in a single pass — and identify the killswitch — in only 34 seconds. Using Google’s Gemini 1.5 Pro, a powerful multimodal AI model, we can help transform code vulnerability detection and remediation, and build a software vulnerability scanning and remediation engine.

While Gemini 1.5 Pro demonstrates promising capabilities in code analysis, it’s important to note that this approach is still experimental. We believe that it is important to explore the potential of this technology for vulnerability detection, and continue development and validation efforts before it can be considered a robust security tool.

Mature security solutions with established quality control and integration into CI/CD workflows are widely available and recommended for production environments. Today, we detail an experiment that aims to highlight the possibilities of generative AI in security. Please note that we do not advocate this solution as a replacement for established and proven security practices.

Exploring code vulnerability scanning with Gemini 1.5 Pro

Using Gemini 1.5 Pro to explore a potential approach to code vulnerability scanning, we can leverage its extended context window — up to 2 million tokens — to analyze large sets of code files stored in a Google Cloud Storage bucket. (In a modern CI/CD pipeline, this code would usually reside in a repository.)

The larger context window enhances the model’s ability to take in more information, resulting in outputs that are more consistent, relevant, and useful. It enables efficient scanning of large codebases, analysis of multiple files in a single call, and a deeper understanding of complex relationships and patterns within the code.

This experimental approach aims to efficiently scan large codebases, analyze multiple files in a single call, and delve deeper into complex code relationships and patternsThe model’s deep analysis of code can help ensure comprehensive vulnerability detection, going beyond surface-level flaws.

By using this approach, we can accommodate code written in several programming languages. Additionally, we can generate the findings and recommendations as JSON or CSV reports, which we would hypothetically use to make comparisons against established benchmarks and policy checks.

A deeper dive: The technical approach

All of the above is important to understand what you’re building. Now, it’s time to build it. We’ve developed a streamlined process to help you get started.

First, Python files are extracted from a specified Google Cloud Storage (GCS) bucket and consolidated into a single string to facilitate analysis. The engine then interacts with Gemini 1.5 Pro using the Vertex AI Python SDK for generative models, and provides a precise prompt to identify vulnerabilities and generate specific output formats.

By combining one-shot-inference with carefully crafted prompt engineering, Gemini 1.5 Pro can analyze the code structure to identify potential vulnerabilities within the code and suggest helpful and contextual modifications.. These findings, along with relevant code snippets, are then extracted from the model’s response and systematically organized in a Pandas DataFrame and finally transformed into CSV and JSON reports, ready for further analysis.

It’s important to note that recommended fixes are not automatically applied.

The scope of this experiment is limited to identifying issues and providing helpful and contextual modification. Automating remediations or fitting the findings into a review workflow would exist in a more mature tool, and hasn’t been considered as part of the experiment.

You can refer to the complete notebook.