Can Machines Dream of Secure Code? [eng]
Do machines hallucinate insecure code? In the blink of an eye we jumped on the AI bandwagon and pivoted from AI skepticism to AI adoption, but what did we trade off exactly? Writing secure code is tougher than it seems and we humans are getting it wrong time and time again. Even highly popular open-source software projects are repeatedly found vulnerable. So how does ChatGPT or GitHub Copilot live up to standards of secure software? developers have already embraced AI for augmented software development but let's challenge those AI tools you've come to rely on day-to-day and see how capable they are in producing secure software.
Liran Tal
Snyk
- Liran Tal is an award-winning software developer, security researcher, and open source champion in the JavaScript community
- He's an internationally recognized GitHub Star, acknowledged for his open source advocacy, and has received the OpenJS Foundation's Pathfinder for Security for his work on Node.js security
- His contributions to developer security education include leading OWASP projects, building supply chain security tools, participation in CNCF and OpenSSF initiatives, and authoring books such as O'Reilly's Serverless Security
- He leads the developer advocacy team at Snyk.io and is on a mission to empower developers with better application security skills
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