Prompt Obfuscation for Large Language Models (2409.11026v3)

Abstract: System prompts that include detailed instructions to describe the task performed by the underlying LLM can easily transform foundation models into tools and services with minimal overhead. Because of their crucial impact on the utility, they are often considered intellectual property, similar to the code of a software product. However, extracting system prompts is easily possible. As of today, there is no effective countermeasure to prevent the stealing of system prompts and all safeguarding efforts could be evaded. In this work, we propose an alternative to conventional system prompts. We introduce prompt obfuscation to prevent the extraction of the system prompt with only little overhead. The core idea is to find a representation of the original system prompt that leads to the same functionality, while the obfuscated system prompt does not contain any information that allows conclusions to be drawn about the original system prompt. We evaluate our approach by comparing our obfuscated prompt output with the output of the original prompt, using eight distinct metrics, to measure the lexical, character-level, and semantic similarity. We show that the obfuscated version is constantly on par with the original one. We further perform three different deobfuscation attacks with varying attacker knowledge--covering both black-box and white-box conditions--and show that in realistic attack scenarios an attacker is not able to extract meaningful information. Overall, we demonstrate that prompt obfuscation is an effective mechanism to safeguard the intellectual property of a system prompt while maintaining the same utility as the original prompt.

• The paper presents a novel prompt obfuscation strategy that conceals sensitive instructions while preserving system functionality.
• It employs optimization in the soft prompt space to maintain performance and exhibits robust resistance against both black-box and white-box deobfuscation attacks.
• Evaluation using BLEU, ROUGE, METEOR, and BERTScore metrics confirms that the approach effectively safeguards intellectual property without compromising output quality.

Prompt Obfuscation in LLMs

Abstract

This paper addresses a critical issue pertaining to the intellectual property protection of system prompts in LLMs. It proposes a novel method termed "prompt obfuscation" which conceals system instructions while preserving their functionality. The method utilizes optimization techniques to find alternative representations of prompts, ensuring that obfuscated prompts do not reveal sensitive information even when exposed to potential prompt injection attacks.

Key Contributions

1. Prompt Obfuscation: The paper introduces a strategy for obfuscating prompts within the embedded soft prompt space. It demonstrates maintenance of utility comparable to original prompts while obfuscating sensitive information.
2. Deobfuscation Resistance: Through various black-box and white-box deobfuscation attack strategies, including prompt injections and token space projections, the robustness of the obfuscation method is evaluated. The obfuscation effectively resists these attacks, only occasionally failing under certain conditions.
3. Diverse Evaluation Metrics: A range of metrics such as BLEU, ROUGE, METEOR, and BERTScore were used to quantify the performance of the obfuscation method across different tasks and stylistic prompts.

Discussion

Prompt obfuscation is positioned as an effective alternative to safeguard system prompts, akin to protecting source code. By leveraging the continuous soft prompt space, the obfuscation disrupts attempts at prompt injection while ensuring that the intended output style and task performance are retained. The approach showed significant improvements in maintaining function across different datasets, including TruthfulQA and CNN/DailyMail.

The paper further explores different obfuscation scenarios based on task and style delineations, revealing varying levels of optimization ease across prompts. Interestingly, the experimental results illustrate minor performance variations due to hyperparameter adjustments, underscoring the approach’s robustness.

Implications and Future Work

The findings suggest that prompt obfuscation could be widely adopted across AI systems to protect proprietary instructions effectively. This would be particularly useful for commercial APIs where safeguarding intellectual property is paramount. Future work might explore more sophisticated gradient-based methods or incorporate user feedback to enhance optimization further.

The paper paves the way for developing more secure LLM-based applications, promoting responsible innovation while addressing IP concerns. Furthermore, it invites comprehensive exploration into adversarial robustness, ensuring systems are resilient not only to prompt injections but also to other conceivable attack vectors.

Conclusion

Prompt obfuscation emerges as a promising technique to reconcile the need for IP protection with the operational efficacy of LLMs. The paper establishes the groundwork for further exploration into LLM security, offering insights that hold significant promise for both theoretical advancements and practical applications in AI.

Overall, the research contributes a meaningful layer of security for LLM-integrated services, ensuring that system prompts, considered a crucial component of the AI's intellectual machinery, remain protected without compromising on intended functionality.