Defending Against Unforeseen Failure Modes with Latent Adversarial Training (2403.05030v4)

Abstract: Despite extensive diagnostics and debugging by developers, AI systems sometimes exhibit harmful unintended behaviors. Finding and fixing these is challenging because the attack surface is so large -- it is not tractable to exhaustively search for inputs that may elicit harmful behaviors. Red-teaming and adversarial training (AT) are commonly used to improve robustness, however, they empirically struggle to fix failure modes that differ from the attacks used during training. In this work, we utilize latent adversarial training (LAT) to defend against vulnerabilities without leveraging knowledge of what they are or using inputs that elicit them. LAT makes use of the compressed, abstract, and structured latent representations of concepts that the network actually uses for prediction. Here, we use it to defend against failure modes without examples that elicit them. Specifically, we use LAT to remove trojans and defend against held-out classes of adversarial attacks. We show in image classification, text classification, and text generation tasks that LAT usually improves both robustness to novel attacks and performance on clean data relative to AT. This suggests that LAT can be a promising tool for defending against failure modes that are not explicitly identified by developers.

Exploring Latent Adversarial Training for Enhanced Model Robustness

Introduction

In the pursuit of advancing artificial intelligence, ensuring the robustness and reliability of AI systems, particularly in the face of adversarial inputs, remains a paramount challenge. Traditional approaches including adversarial training (AT) have aimed at enhancing model resilience but often fall short when confronted with unforeseen failure modes post-deployment. In response to these limitations, a novel approach, termed Latent Adversarial Training (LAT), has been introduced, leveraging the latent spaces of neural networks to fortify models against vulnerabilities without necessitating explicit examples of failure-triggering inputs. This exploration unfolds within image classification, text classification, and text generation domains, revealing that LAT generally surpasses conventional AT in maintaining performance on clean data while bolstering robustness against both trojans and novel classes of adversarial attacks.

Methodology

At its core, LAT diverges from the traditional AT by administering adversarial perturbations within the model's latent space rather than its input space. This distinction emerges from a recognition of the compressed, abstract nature of latent representations in machines, potentially enabling a broader and more effective defensive mechanism against a spectrum of unforeseen adversarial tactics. The experimentation conducted spans across multiple domains, wherein models were initially fine-tuned with poisoned data to incorporate trojans, succeeded by further fine-tuning under the regimes of LAT, AT, and random latent perturbations. These models were then evaluated on clean data, under novel adversarial conditions, and in the presence of trojans to assess the efficacy of LAT in comparation to existing practices.

Key Findings

The empirical evidence gathered through this paper elucidates several compelling insights. It was observed that LAT consistently enhances model robustness against novel adversarial attacks and trojans without compromising, and occasionally improving, performance on clean data. This suggests that LAT not only serves as a robust defensive tactic but also contributes to the overall model performance and reliability. Notably, these advantages were realized across varied tasks and models, reinforcing the potential of LAT as a universally applicable strategy for AI safety and reliability. However, it was also recognized that the selection of the appropriate latent layer for perturbation is crucial, indicating that further research into optimal layer selection could augment the utility of LAT.

Implications and Future Directions

The introduction and validation of LAT as a viable strategy for defending against unforeseen adversarial scenarios herald a significant stride in AI safety research. By shifting the focus from input space to latent space perturbations, LAT addresses the intrinsic challenge of predicting and preparing for the myriad of potential failure modes that may not be evident during model development. This approach not only enhances the robustness of models but also underscores the complexity and multidimensionality of securing AI systems against adversarial threats.

Future investigations could delve into refining the methodologies for latent layer selection, expanding the applicability of LAT across a broader spectrum of models and domains, and exploring the intersection of LAT with other defensive mechanisms. Additionally, the exploration of targeted adversarial attacks within the latent space presents an intriguing avenue for further research, potentially offering insights into model vulnerabilities and resilience in unprecedented detail.

Conclusion

The findings of this paper present a promising avenue towards fortifying AI models against the elusive and ever-evolving landscape of adversarial threats. Latent Adversarial Training emerges not only as a technique for enhancing model robustness but also as a catalyst for further exploration in the domain of AI safety. As we venture into increasingly complex and high-stakes applications of AI, the quest for robust, reliable models becomes ever more critical, with methodologies like LAT playing a pivotal role in realizing this objective.