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Securing the Software Supply Chain with LLMs

May 3, 2024

Feross Aboukhadijeh from Socket discusses using large language models to secure the software supply chain, overcoming challenges like the recent XZutils attack. They explore how AI tools can help identify risky packages, cut down on noise, and make security problems tractable. The conversation dives into the role of LLMs in scanning open source code, improving security maturity with NIST standards, and the evolving landscape of security against advanced attackers.

38:57

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Episode guests

Feross Aboukhadijeh

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Episode notes

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Quick takeaways

LLMs can help scan open-source code for risks and reduce noise for human review.

AI integration in security tools enhances threat detection and response capabilities.

Deep dives

The Rise of Sophisticated Attacks

Attackers are increasingly using sophisticated tactics to penetrate supply chains and gain control over integral components early in the development lifecycle. Known vulnerabilities are not sufficient to stop these attacks, as traditional solutions in the supply chain industry are often basic and inadequate to address complex threats.

Introduction

3min

Challenges in Securing Software Supply Chains

13min

AI's Role in Improving Supply Chain Security

6min

Navigating the Complexities of the Software Security Market

2min

Enhancing Security Maturity with NIST Standards and AI Integration

4min

AI in Software Supply Chain Security

11min

Socket Founder and CEO Feross Aboukhadijeh joins a16z's Joel de la Garza and Derrick Harris to discuss the open-source software supply chain. Feross and Joel share their thoughts and insights on topics ranging from the recent XZutils attack to how large language models can help overcome understaffed security teams and overwhelmed developers.

Despite some increasingly sophisticated attacks making headlines and compromising countless systems, they're optimistic that LLMs, in particular, could be a turning point for security blue teams. As Feross sums up one possibility:

"The way we think about gen AI on the defensive side is that it's not as good as a human looking at the code, but it's something. . . . Our challenge is that we want to scan all the open source code that exists out there. That is not something you can pay humans to do. That is not scalable at all. But, with the right techniques, with the right pre-filtering stages, you can actually put a lot of that stuff through LLMs and out the other side will pop a list of of risky packages.

"And then that's a much smaller number that you can have humans take a look at. And so we're using it as a tool . . . to find the needle in the haystack, what is worth looking at. It's not perfect, but it can help cut down on the noise and it can even make this problem tractable, which previously wasn't even tractable."

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