Ignore Previous Prompt: Attack Techniques For Language Models (2211.09527v1)

Abstract: Transformer-based LLMs provide a powerful foundation for natural language tasks in large-scale customer-facing applications. However, studies that explore their vulnerabilities emerging from malicious user interaction are scarce. By proposing PromptInject, a prosaic alignment framework for mask-based iterative adversarial prompt composition, we examine how GPT-3, the most widely deployed LLM in production, can be easily misaligned by simple handcrafted inputs. In particular, we investigate two types of attacks -- goal hijacking and prompt leaking -- and demonstrate that even low-aptitude, but sufficiently ill-intentioned agents, can easily exploit GPT-3's stochastic nature, creating long-tail risks. The code for PromptInject is available at https://github.com/agencyenterprise/PromptInject.

Overview of "Ignore Previous Prompt: Attack Techniques For LLMs"

The paper "Ignore Previous Prompt: Attack Techniques For LLMs" presents an in-depth analysis of vulnerabilities in transformer-based LLMs, specifically GPT-3, under adversarial conditions. The paper introduces a novel framework named PromptInject, designed to simulate and evaluate the robustness of LLMs against prompt injection attacks. These attacks, categorized into goal hijacking and prompt leaking, demonstrate the capacity for malicious users to intentionally misalign model outputs, presenting significant risks for widespread deployment applications.

Methodology and Experimental Design

PromptInject Framework

The proposed framework allows for the generation of adversarial prompts through a modular assembly process, testing the LLMs' susceptibility to adversarial inputs. The framework takes into account variables such as:

• Base Prompt: Initial instruction and examples that guide the model.
• Attack Prompt: Maliciously crafted instructions attempting to derail the model.
• Model Settings: Parameters like temperature, top-p sampling, and penalty settings that influence the stochastic nature of LLM outputs.

Attack Types:

1. Goal Hijacking: Redirecting the model to produce a predetermined target phrase.
2. Prompt Leaking: Forcing the model to expose part or all of the original prompt instructions.

Experimental Setup

The framework was validated through various experimentations using GPT-3's text-davinci-002, exploring models' responses to crafted inputs based on OpenAI's published prompt examples. The attack success rate for different components and settings was statistically evaluated, providing insights into the factors affecting model vulnerability.

Key Findings

1. Susceptibility to Attack Prompts: The structure of the attack prompt significantly impacted success rates, indicating the intricacies of language processing in LLMs.
2. Impact of Model Settings: Temperature adjustment exhibited an influence on attack prevention, while top-p and frequency/presence penalties showed minimal effect.
3. Delimiter Efficacy: Use of delimiters enhanced the attack's effectiveness, although the specific configuration of delimiters showed variable results.
4. Strength of String Influence: Stronger, potentially harmful rogue strings surprisingly lowered attack success, suggesting possible in-built bias mitigation strategies within LLMs like GPT-3.
5. Differences Among Models: text-davinci-002 displayed the highest vulnerability compared to other model variants, pointing to a correlation between model capability and susceptibility to sophisticated linguistic inputs.

Implications

Practical Implications:

The research underscores the necessity for robust defense mechanisms against adversarial attacks in AI systems. With LLMs being integral to applications like chatbots and automated content generation, ensuring the alignment and safety of these models is paramount, particularly when exposed to public inputs.

Theoretical Implications:

The findings open avenues for further exploration into the alignment problem, focusing on enhancing model understanding and control over unintentional behaviors elicited by adversarial inputs. Prompt injection serves as a compelling case paper for investigating model vulnerabilities at the intersection of language understanding and alignment.

Future Directions

The authors propose expanding the framework to explore other emerging LLMs and investigate new defensive strategies, including fine-tuning approaches and hybrid moderation models. The code's public release for PromptInject aims to facilitate continuous research efforts in assessing and mitigating AI-associated risks, fostering safer AI deployments.

In conclusion, this paper critically examines the robustness of LLMs against specific attack vectors, providing substantial insights into potential vulnerabilities and laying the groundwork for future research in AI safety.