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

arXiv: https://arxiv.org/abs/2409.03131 · HTML: https://mlsanity.com/paper/2409.03131/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 3 references that are in the map.
- First posted on arXiv 4 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack](https://mlsanity.com/paper/2404.01833/) (2024)
- [Multi-step Jailbreaking Privacy Attacks on ChatGPT](https://mlsanity.com/paper/2304.05197/) (2023)
- [Multi-Turn Context Jailbreak Attack on Large Language Models From First Principles](https://mlsanity.com/paper/2408.04686/) (2024)

## Built on by (most cited first, 5 of 5)

- [Breaking ReAct Agents: Foot-in-the-Door Attack Will Get You In](https://mlsanity.com/paper/2410.16950/) (2024)
- [Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI](https://mlsanity.com/paper/2409.15398/) (2024)
- [Layered Chain-of-Thought Prompting for Multi-Agent LLM Systems: A Comprehensive Approach to Explainable Large Language Models](https://mlsanity.com/paper/2501.18645/) (2025)
- [A Representation Engineering Perspective on the Effectiveness of Multi-Turn Jailbreaks](https://mlsanity.com/paper/2507.02956/) (2025)
- [An indicator for effectiveness of text-to-image guardrails utilizing the Single-Turn Crescendo Attack (STCA)](https://mlsanity.com/paper/2411.18699/) (2024)

## Abstract (arXiv)

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.