Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition

Original Paper: https://arxiv.org/abs/2311.16119

By: Sander Schulhoff, Jeremy Pinto, Anaum Khan, Louis-François Bouchard, Chenglei Si, Svetlina Anati, Valen Tagliabue, Anson Liu Kost, Christopher Carnahan, Jordan Boyd-Graber

Abstract:

Large Language Models (LLMs) are deployed in interactive contexts with direct user engagement, such as chatbots and writing assistants. These deployments are vulnerable to prompt injection and jailbreaking (collectively, prompt hacking), in which models are manipulated to ignore their original instructions and follow potentially malicious ones. Although widely acknowledged as a significant security threat, there is a dearth of large-scale resources and quantitative studies on prompt hacking. To address this lacuna, we launch a global prompt hacking competition, which allows for free-form human input attacks. We elicit 600K+ adversarial prompts against three state-of-the-art LLMs. We describe the dataset, which empirically verifies that current LLMs can indeed be manipulated via prompt hacking. We also present a comprehensive taxonomical ontology of the types of adversarial prompts.

Summary Notes

Strengthening LLMs Against Prompt Hacking: Lessons from a Global Competition

Large Language Models (LLMs) like GPT-4 and BLOOM are transforming industries with their advanced AI capabilities. Despite their benefits, they’re vulnerable to prompt hacking, a method where attackers craft specific inputs to manipulate outputs.

The global HackAPrompt competition, with over 2,800 participants and 600,000 adversarial prompts, has brought these vulnerabilities to light. This post explores the competition’s findings and offers practical advice for AI Engineers on protecting LLMs.

Understanding Prompt Hacking

Prompt hacking exploits LLMs by feeding them manipulated inputs, posing a threat to data and system security.

Traditional security isn't always effective against these sophisticated attacks. The HackAPrompt competition aimed to fill this knowledge gap by systematically examining LLM robustness against such threats.

Key Takeaways from HackAPrompt

The competition revealed:

• Widespread Vulnerabilities: Many adversarial prompts successfully tricked the LLMs, exposing a systemic weakness.
• Types of Attacks: A developed taxonomy categorizes the attacks, helping understand and address prompt hacking more effectively.
• Effective Prompts: Certain prompts were particularly effective in altering outputs, highlighting specific areas of concern.

Enhancing LLM Security: Practical Advice

Based on the competition's insights, here are strategies for AI Engineers:

• Robustness Testing: Regular testing against adversarially crafted prompts is crucial. Use the developed taxonomy to guide these efforts.
• Improved Input Validation: Establish advanced input validation to detect and neutralize malicious prompts.
• Continuous Monitoring: Monitor for unusual model behavior to detect prompt hacking attempts early.
• Community Engagement: Engage with initiatives like HackAPrompt to stay updated on threats and solutions.
• R&D Investment: Allocate resources to research and develop more secure LLMs, focusing on innovative prompt hacking mitigation strategies.

Conclusion

The HackAPrompt competition underscores the collective effort needed to address LLM vulnerabilities.

By applying the insights and strategies derived from the competition, AI Engineers can better protect against prompt hacking. Embracing these lessons is key to leveraging LLMs’ full potential securely.

Acknowledgements

The dedication of the participants and the support from various organizations have been crucial in advancing our understanding of LLM vulnerabilities to prompt hacking.

Their contributions are invaluable to enhancing AI security.