Robust Safety Classifier for Large Language Models: Adversarial Prompt Shield

Original Paper: https://arxiv.org/abs/2311.00172

By: Jinhwa Kim, Ali Derakhshan, Ian G. Harris

Abstract:

Large Language Models' safety remains a critical concern due to their vulnerability to adversarial attacks, which can prompt these systems to produce harmful responses. In the heart of these systems lies a safety classifier, a computational model trained to discern and mitigate potentially harmful, offensive, or unethical outputs. However, contemporary safety classifiers, despite their potential, often fail when exposed to inputs infused with adversarial noise. In response, our study introduces the Adversarial Prompt Shield (APS), a lightweight model that excels in detection accuracy and demonstrates resilience against adversarial prompts. Additionally, we propose novel strategies for autonomously generating adversarial training datasets, named Bot Adversarial Noisy Dialogue (BAND) datasets. These datasets are designed to fortify the safety classifier's robustness, and we investigate the consequences of incorporating adversarial examples into the training process. Through evaluations involving Large Language Models, we demonstrate that our classifier has the potential to decrease the attack success rate resulting from adversarial attacks by up to 60%. This advancement paves the way for the next generation of more reliable and resilient conversational agents.

Summary Notes

Strengthening Large Language Model Safety with Adversarial Prompt Shield

The evolution of artificial intelligence has brought Large Language Models (LLMs) to the forefront, powering diverse applications from chatbots to advanced content generators.

However, as these models become more integrated into daily tasks, their vulnerability to adversarial attacks is a growing concern, compromising AI system integrity and safety.

This blog explores the Adversarial Prompt Shield (APS), a novel solution enhancing LLM safety.

Understanding Adversarial Threats

Adversarial attacks aim to manipulate AI models into producing unwanted outcomes, such as generating harmful or biased content.

Recognizing and addressing these threats is vital for maintaining real-world application integrity.

Current Defense Limitations

Existing defenses like perplexity filters and paraphrasing struggle against modern adversarial tactics, often reducing model performance and increasing false positives.

Introducing Adversarial Prompt Shield (APS)

APS introduces a groundbreaking approach to protect LLMs from adversarial inputs. Key features include:

• Efficiency with DistilBERT: APS leverages DistilBERT for high performance with less resource demand, suitable for real-time use.
• Binary Classification: A simple yet effective system to differentiate safe from harmful prompts.

Training with Bot Adversarial Noisy Dialogue (BAND)

APS's strength lies in its training method, using BAND to introduce "noise" into dialogue datasets. This simulates potential adversarial inputs, improving APS's detection capabilities without costly data generation.

Superior Classifier Results

Testing proves APS's effectiveness, outperforming existing safety classifiers by maintaining safety standards without compromising response quality.

Key Performance Highlights:

• Robustness: APS demonstrates consistent effectiveness across various adversarial scenarios.
• Quality Preservation: It balances safety with maintaining user experience quality.

Enhancing LLM Safety

APS's impact goes beyond a single classifier, offering a new standard for LLM safety enhancements. By incorporating APS, LLMs can better resist adversarial attacks, leading to more reliable AI applications.

Future Directions

The success of APS encourages further research into advanced adversarial training and real-time detection, aiming for an AI ecosystem where safety and performance harmoniously coexist.

Conclusion: Towards a Safer AI Future with APS

The Adversarial Prompt Shield sets a new benchmark for secure AI technology, improving LLM safety and laying the groundwork for future AI system development.

Continuous advancements in APS and similar technologies are crucial for unleashing AI's full potential safely.