LLMs are becoming more and more popular across all industries and that creates a new attack surface for attackers to target to misuse for malicious purposes. To prevent this LLM models have multiple layers of defenses (with more being created every day), one of the layers attempts to limit the capability of the LLM to what the developer intended. For example, a LLM running a chat service for software support would be limited to answer questions about software identified by the developer. Attackers attempt to bypass these safeguards with the intent to achieve unauthorized actions or “jailbreak” the LLM. Depending on the LLM, this can be easy or complicated.
Earlier this month Microsoft published a paper showcasing the “Crescendo” LLM jailbreak method called “Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack“. Using this method a successful attack could usually be completed in a chain of fewer than 10 interaction turns.
Large Language Models (LLMs) have risen significantly in popularity and are increasingly being adopted across multiple applications. These LLMs are heavily aligned to resist engaging in illegal or unethical topics as a means to avoid contributing to responsible AI harms. However, a recent line of attacks, known as “jailbreaks”, seek to overcome this alignment. Intuitively, jailbreak attacks aim to narrow the gap between what the model can do and what it is willing to do. In this paper, we introduce a novel jailbreak attack called Crescendo. Unlike existing jailbreak methods, Crescendo is a multi-turn jailbreak that interacts with the model in a seemingly benign manner. It begins with a general prompt or question about the task at hand and then gradually escalates the dialogue by referencing the model’s replies, progressively leading to a successful jailbreak. We evaluate Crescendo on various public systems, including ChatGPT, Gemini Pro, Gemini-Ultra, LlaMA-2 70b Chat, and Anthropic Chat. Our results demonstrate the strong efficacy of Crescendo, with it achieving high attack success rates across all evaluated models and tasks. Furthermore, we introduce Crescendomation, a tool that automates the Crescendo attack, and our evaluation showcases its effectiveness against state-of-the-art models.
Microsoft has also published a Blog post that goes over this attack and potential mitigation steps that can be implemented along with details on new tools developed to counter this attack using their “AI Watchdog” and “AI Spotlight” features. The tools attempt to identify adversarial content in both input and outputs to prevent prompt injection attacks.
SCM Magazine has a good writeup on the attack and the defenses against it.
– Suramya
Source: Slashdot: ‘Crescendo’ Method Can Jailbreak LLMs Using Seemingly Benign Prompts
