Snyk: Model Red-Teaming: Dynamic Security Analysis for LLMs

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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.