Large Language Models (LLMs) have become prevalent across diverse sectors,
transforming human life with their extraordinary reasoning and comprehension
abilities. As they find increased use in sensitive tasks, safety concerns have
gained widespread attention. Extensive efforts have been dedicated to aligning
LLMs with human moral principles to ensure their safe deployment. Despite their
potential, recent research indicates aligned LLMs are prone to specialized
jailbreaking prompts that bypass safety measures to elicit violent and harmful
content. The intrinsic discrete nature and substantial scale of contemporary
LLMs pose significant challenges in automatically generating diverse,
efficient, and potent jailbreaking prompts, representing a continuous obstacle.
In this paper, we introduce RIPPLE (Rapid Optimization via Subconscious
Exploitation and Echopraxia), a novel optimization-based method inspired by two
psychological concepts: subconsciousness and echopraxia, which describe the
processes of the mind that occur without conscious awareness and the
involuntary mimicry of actions, respectively. Evaluations across 6 open-source
LLMs and 4 commercial LLM APIs show RIPPLE achieves an average Attack Success
Rate of 91.5\%, outperforming five current methods by up to 47.0\% with an 8x
reduction in overhead. Furthermore, it displays significant transferability and
stealth, successfully evading established detection mechanisms. The code of our
work is available at
https://github.com/SolidShen/RIPPLE_official/tree/official

通过 RIPPLE 方法，我们展示了对大型语言模型进行有效攻击的可能性，它涉及到大型语言模型的安全问题以及心理概念的应用。

通过潜意识利用和外部反映实现对 LLMs 的快速破解优化

Rapid Optimization for Jailbreaking LLMs via Subconscious Exploitation  and Echopraxia

Recently, NLP has seen a surge in the usage of large pre-trained models.
Users download weights of models pre-trained on large datasets, then fine-tune
the weights on a task of their choice. This raises the question of whether
downloading untrusted pre-trained weights can pose a security threat. In this
paper, we show that it is possible to construct ``weight poisoning'' attacks
where pre-trained weights are injected with vulnerabilities that expose
``backdoors'' after fine-tuning, enabling the attacker to manipulate the model
prediction simply by injecting an arbitrary keyword. We show that by applying a
regularization method, which we call RIPPLe, and an initialization procedure,
which we call Embedding Surgery, such attacks are possible even with limited
knowledge of the dataset and fine-tuning procedure. Our experiments on
sentiment classification, toxicity detection, and spam detection show that this
attack is widely applicable and poses a serious threat. Finally, we outline
practical defenses against such attacks. Code to reproduce our experiments is
available at this https URL

该研究重点讨论了使用大型预先训练模型的安全风险，提出了一种称为 RIPPLe 的正则化方法和一种称为嵌入手术的初始化方法，以防止称为权重污染的攻击，该攻击可以注入安全漏洞。进一步实验证明该攻击可能性很高，对多种应用都带来了严重威胁。