Prompt Breakers: Exploiting Weaknesses in AI’s In-Context Learning
![Traditional natural language processing attacks demonstrate limited transferability and reduced effectiveness against in-context learning classifiers-as evidenced by consistently lower attack success rates (measured as Attack Success Rate [latex]ASR[/latex] and robust [latex]rASR[/latex]) across varying perturbation budgets-highlighting the need for attack strategies specifically designed for this emerging paradigm.](https://arxiv.org/html/2601.21586v1/x44.png)
New research reveals how cleverly crafted prompts can bypass safeguards in large language models relying on in-context learning, posing a significant security risk.
![Traditional natural language processing attacks demonstrate limited transferability and reduced effectiveness against in-context learning classifiers-as evidenced by consistently lower attack success rates (measured as Attack Success Rate [latex]ASR[/latex] and robust [latex]rASR[/latex]) across varying perturbation budgets-highlighting the need for attack strategies specifically designed for this emerging paradigm.](https://arxiv.org/html/2601.21586v1/x44.png)
New research reveals how cleverly crafted prompts can bypass safeguards in large language models relying on in-context learning, posing a significant security risk.
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