LIVE WEBINAR

Model Red-Teaming: Dynamic Security Analysis for LLMs

Wednesday, December 10th at 10:00 AM GMT / 11:00 AM CET

Join us for a delicious, shared learning experience! Register today and we'll send you a complimentary brunch box that we can enjoy together while you learn about emerging LLM red-teaming techniques. Savor your gifted treat as we dive into critical security insights.

The rise of Large Language Models has many organizations rushing to integrate AI-powered tools into existing products, but they introduce significant new risk. OWASP has recently introduced the LLM Top 10 to highlight these novel threat vectors, including prompt injection and data exfiltration. However, existing AppSec tools are not designed to detect and remediate these vulnerabilities. In particular, static analysis (SAST), one of the most common tools, cannot be used since there is no code: machine-learning models are effectively “black boxes."

LLM red-teaming is emerging as a technique to minimize the vulnerabilities associated with LLM adoption, ensure data confidentiality, and verify that safety and ethical guardrails are being applied. It applies tactics associated with penetration testing and dynamic analysis (DAST) of traditional software to the new world of machine-learning models.

Save Your Spot

Join Snyk, the leader in AI Trust, for an overview of LLM red-teaming principles while enjoying your brunch box.

What We'll Cover While We Brunch:

What are some of the novel threat vectors associated with large language models, and how are these attacks carried out?
Why are traditional vulnerability-detection tools (such as SAST and SCA) incapable of detecting the most serious risks in LLMs?
How can the principles of traditional dynamic analysis be applied to machine learning models, and what types of new tools are needed?
How should organizations begin to approach building an effective program for LLM red-teaming?

Join us for a 30-minute presentation starting at 10:00 AM GMT / 11:00 AM CET. We'll follow that with a 15-minute live Q&A session.