Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs

Understand

Despite the remarkable performance of Large Language Models (LLMs), they remain vulnerable to jailbreak attacks, which can compromise their safety mechanisms.

• Existing studies often rely on brute-force optimization or manual design, failing to uncover potential risks in real-world scenarios.
• To address this, we propose a novel jailbreak attack framework, ICRT, inspired by heuristics and biases in human cognition.
• Leveraging the simplicity effect, we employ cognitive decomposition to reduce the complexity of malicious prompts.