Source URL: https://simonwillison.net/2024/Nov/1/from-naptime-to-big-sleep/#atom-everything
Source: Simon Willison’s Weblog
Title: From Naptime to Big Sleep: Using Large Language Models To Catch Vulnerabilities In Real-World Code
Feedly Summary: From Naptime to Big Sleep: Using Large Language Models To Catch Vulnerabilities In Real-World Code
Google’s Project Zero security team used a system based around Gemini 1.5 Pro to find a previously unreported security vulnerability in SQLite (a stack buffer underflow), in time for it to be fixed prior to making it into a release.
A key insight here is that LLMs are well suited for checking for new variants of previously reported vulnerabilities:
A key motivating factor for Naptime and now for Big Sleep has been the continued in-the-wild discovery of exploits for variants of previously found and patched vulnerabilities. As this trend continues, it’s clear that fuzzing is not succeeding at catching such variants, and that for attackers, manual variant analysis is a cost-effective approach.
We also feel that this variant-analysis task is a better fit for current LLMs than the more general open-ended vulnerability research problem. By providing a starting point – such as the details of a previously fixed vulnerability – we remove a lot of ambiguity from vulnerability research, and start from a concrete, well-founded theory: “This was a previous bug; there is probably another similar one somewhere".
LLMs are great at pattern matching. It turns out feeding in a pattern describing a prior vulnerability is a great way to identify potential new ones.
Via Hacker News
Tags: gemini, security, sqlite, google, generative-ai, ai, llms, prompt-engineering
AI Summary and Description: Yes
Summary: The text discusses Google’s Project Zero team leveraging Large Language Models (LLMs), specifically Gemini 1.5 Pro, to discover vulnerabilities in code. It highlights the effectiveness of LLMs in identifying new variants of previously reported vulnerabilities, a gap left by traditional fuzzing methods, thus contributing to improved security practices.
Detailed Description: The article outlines the innovative use of LLMs in the field of vulnerability detection, emphasizing their capability to address specific security concerns that traditional methods might overlook. Key points include:
– **Use Case**: The Project Zero team utilized LLM technology to detect a previously uncaught security vulnerability in SQLite, illustrating the model’s practical application in real-world scenarios.
– **Concept of Variant Analysis**: As vulnerabilities evolve, so do the methods to identify them. This text discusses how LLMs are particularly suited for variant analysis of already known vulnerabilities, marking a shift from more generic vulnerability research.
– **Limitations of Fuzzing**: Traditional fuzzing techniques have shown limited success in identifying new variants of patched vulnerabilities. This reinforces the need for advanced techniques, like those provided by LLMs.
– **Pattern Matching Capability**: LLMs excel in pattern recognition. By providing them with examples of previously detected vulnerabilities, they can more accurately predict and identify similar security issues, enhancing the security landscape.
Overall, this development demonstrates a significant advancement in software security practices, offering security professionals new tools to maintain system integrity and reduce vulnerabilities in code. The use of LLMs for such targeted analysis encourages a shift in how organizations approach vulnerability detection and remediation efforts, highlighting a proactive stance in software security.
– This approach can lead to fewer exploits in the wild, significantly reducing the risk associated with known vulnerabilities.
– Integration of AI-driven solutions will likely encourage others in the field to explore similar methodologies, making LLMs a valuable asset in the ongoing battle against emerging threats in software security.