An indicator for effectiveness of text-to-image guardrails utilizing the Single-Turn Crescendo Attack (STCA)

• URL: http://arxiv.org/abs/2411.18699v1
• Date: Wed, 27 Nov 2024 19:09:16 GMT
• Title: An indicator for effectiveness of text-to-image guardrails utilizing the Single-Turn Crescendo Attack (STCA)
• Authors: Ted Kwartler, Nataliia Bagan, Ivan Banny, Alan Aqrawi, Arian Abbasi,
• Abstract summary: Single-Turn Crescendo Attack (STCA) is an innovative method designed to bypass the ethical safeguards of text-to-text AI models. This study provides a framework for researchers to rigorously evaluate the robustness of guardrails in text-to-image models.
• Score: 0.0
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• Abstract: The Single-Turn Crescendo Attack (STCA), first introduced in Aqrawi and Abbasi [2024], is an innovative method designed to bypass the ethical safeguards of text-to-text AI models, compelling them to generate harmful content. This technique leverages a strategic escalation of context within a single prompt, combined with trust-building mechanisms, to subtly deceive the model into producing unintended outputs. Extending the application of STCA to text-to-image models, we demonstrate its efficacy by compromising the guardrails of a widely-used model, DALL-E 3, achieving outputs comparable to outputs from the uncensored model Flux Schnell, which served as a baseline control. This study provides a framework for researchers to rigorously evaluate the robustness of guardrails in text-to-image models and benchmark their resilience against adversarial attacks.

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