During red-team evaluation of a Claude-based content generation platform, an adversarial researcher discovers a 'many-shot jailbreak' — by including 100+ examples in the conversation history of harmful content being generated without refusal, subsequent requests for similar harmful content succeed at a 73% rate (vs 8% baseline). The platform allows users to import conversation history from external sources. What is the most effective architectural defense against many-shot jailbreaking through imported conversation history?

Conversation history sanitization is the architecturally correct and most effective defense that balances security with functionality. Removing harmful content examples from imported history before they reach Claude's context prevents the many-shot pattern from being established — Claude cannot be conditioned by examples it never sees. This is upstream removal: sanitize the vector before it can influence the model. This preserves legitimate conversation history import for valid use cases while specifically targeting the harmful content examples that enable the attack. The options have 5 choices here — let me evaluate: Option A (10-turn limit) is too blunt — it prevents legitimate long conversation imports and a 10-turn attack with extremely explicit examples might still succeed. Option C (disable import) solves the security problem by removing the feature — acceptable if the feature isn't critical, but the question implies it's a desired platform capability. Option D (system prompt instruction) is a model-behavior defense that many-shot jailbreaks are specifically designed to override — extensive in-context examples can shift model behavior despite system prompt instructions. Option E (distribution classifier) is a good supplementary layer but anomaly detection has false positive/negative rates — legitimate conversations might flag, and sophisticated attacks might evade. Option B sanitizes the specific harmful content examples that enable the attack without removing legitimate history.