The Dark Side of AI Revealed | 2. Many Shot Jailbreaking скачать в хорошем качестве

The Dark Side of AI Revealed | 2. Many Shot Jailbreaking

In this lesson you’ll learn Many-Shot Jailbreaking, a technique that uses lots of in-context examples to steer an LLM into breaking its own safety rules...and why this becomes more effective as models get larger context windows. We’ll walk through research findings showing how “few-shot” jailbreak attempts often fail, but many-shot (dozens to hundreds of shots) can dramatically increase unsafe outputs across multiple harm categories. Then we’ll cover two simple, practical mitigations you can use when building LLM apps: In-Context Defense (ICD) and Cautionary Warning Defense (CWD). What you’ll learn: What Many-Shot Jailbreaking is (and how it differs from few-shot prompting) Why long context windows are both a superpower and a security risk How harmful response rates can rise as the number of “shots” increases Why this isn’t model-specific (works across multiple model families) Two mitigation patterns you can apply today: ICD (In-Context Defense): prepend refusal examples CWD (Cautionary Warning Defense): add safety warnings before/after the prompt The big open question: why this works - and what that means for alignment research? Why it matters: If you’re shipping AI features in production, “mo context, mo problems” is real: bigger context windows can unlock amazing capabilities, but they also make it easier for adversarial inputs to shape model behaviour in unexpected ways. Resources: Paper: Many-shot Jailbreaking: https://cdn.sanity.io/files/4zrzovbb/...