Unlocking Safer AI: Your Two-Part Field Guide
Blog post from Arize
Large language models are transforming product development and simultaneously becoming targets for adversarial attacks. Sofia Jakovcevic, an AI Solutions Engineer at Arize AI, authored a two-part guide to assist teams in understanding and defending against these threats. The first part focuses on jailbreaks, providing insights from red-teaming experiences to help identify potential vulnerabilities such as system-prompt leaks and emotional manipulations, illustrated through live examples. The second part serves as a practical guide for implementing guardrails to safeguard AI systems, discussing various defensive strategies like keyword bans, ML-based detectors, and LLM moderation, highlighting the importance of observability, and offering resources like a GitHub repository for ongoing guardrail tuning. This comprehensive approach equips teams to anticipate and mitigate vulnerabilities, enabling them to deploy AI solutions securely and confidently.