Well, that escalated quickly: The Single-Turn Crescendo Attack (STCA)

This paper introduces a new method for adversarial attacks on large language models (LLMs) called the Single-Turn Crescendo Attack (STCA). Building on the multi-turn crescendo attack method introduced by Russinovich, Salem, and Eldan (2024), which gradually escalates the context to provoke harmful responses, the STCA achieves similar outcomes in a single interaction. By condensing the escalation into a single, well-crafted prompt, the STCA bypasses typical moderation filters that LLMs use to prevent inappropriate outputs. This technique reveals vulnerabilities in current LLMs and emphasizes the importance of stronger safeguards in responsible AI (RAI). The STCA offers a novel method that has not been previously explored. Figure 1: A direct prompt asking the model to create sentences with the word "fuck" or generate a hypothetical scenario where a world-renowned musician is related to Hitler, would result in the model rejecting the request outright. However, the STCA enables a more condensed, escalating structure, allowing the model to gradually accept the input and continue the dialogue while bypassing its typical moderation filters. For the above examples, we used just 3 turns within a single STCA prompt to achieve this, referred to as an STCA-3.