Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks
Jan 30, 204 | Link to paper
This paper introduces Robust Prompt Optimization (RPO), a novel strategy designed to safeguard large language models (LLMs) from jailbreaking attacks. Jailbreaking, in this context, refers to the process of manipulating an LLM to produce unsafe or undesired outputs. RPO works by optimizing input prompts with gradient-based token optimization, adding tailored suffixes that guide the LLM towards generating safer responses. The research showcases the application of RPO across several LLMs, including Starling-7B and GPT-4, and presents a significant reduction in the success rate of jailbreaking attempts without hampering the model's general performance.
Why this matters
RPO represents a critical step forward in LLM security, offering a practical and effective defense mechanism that can be seamlessly integrated into existing systems. By addressing the vulnerability of LLMs to manipulation, this method enhances the overall safety and reliability of these models, making them more suitable for a wide range of applications.
This paper uses a threat model that much more closely maps to “in the wild” attacks. Although the methods of evaluation are formal (i.e. academic) by their use of this more realistic attack scenario, this research looks to be more applicable to real-world defense.
The defense goal is to resist manipulation by generating outputs that are both safe and resilient to adversarial attacks. Again, this is formally defined by is applicable in real-world terms.
The defense technique they propose happens at the prompt level. This is in contrast to other research that requires model modifications. This makes this defense more accessible.
Methodology
The study employs a gradient-based token optimization technique to adjust the input prompts used with LLMs. By automatically generating and appending specific suffixes to prompts, RPO effectively steers the model's output towards safer and more desired directions. The effectiveness of this approach was tested through extensive experiments involving various attack scenarios and model configurations.
Unfortunately for security practitioners, there is not just a simple list of these defensive suffixes (one example below) - and I suppose just releasing a static list would allow quick adaptations by attackers - however their code is available on GitHub. Going through that repo is out of scope for this article, but I will do that in future and more clearly explain their algorithms - reading code is much simpler than reading formulas, for me.
Key Findings
Proposes a more realistic LLM threat model than previous research
RPO significantly reduces the effectiveness of jailbreaking attacks on LLMs by optimizing input prompts to enforce safe outputs.
Maintains general performance and utility of LLMs, ensuring that the defensive mechanism does not degrade the model's ability to perform its intended tasks.
Versatile across different models and attack types, indicating the potential for broad application in enhancing the security of LLM deployments.
Indications that the findings transfer to black-box models such as GPT4
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