Mechanistic Interpretability for Adversarial Robustness — A Proposal

A research proposal exploring the synergies between mechanistic interpretability and adversarial robustness to develop safer AI systems.

This research proposal explores synergies between mechanistic interpretability and adversarial robustness in AI safety. We review theories of adversarial vulnerability and connections between model interpretability and robustness. Our objectives include investigating feature superposition, reverse-engineering robust models, developing interpretability-guided vulnerability mitigation, and designing training methods that enhance both robustness and interpretability. We propose experiments combining mechanistic interpretability with adversarial robustness. Potential impacts include improved AI safety, vulnerability detection, and model transparency. We address ethical considerations and risks. This interdisciplinary approach aims to develop AI systems that are powerful, transparent, reliable, and aligned with human values, with near-term implications for AI integration in critical domains and long-term outcomes for beneficial AI.

Introduction

AI systems are rapidly growing more general and capable , raising concerns about potential catastrophic risks from future advanced systems. Beyond these long-term existential concerns, as AI systems integrate into critical domains in the near-term – like energy , finance , healthcare , transportation , cybersecurity and military , and telecommunications – ensuring their safety is crucial now.

Safe AI needs to be robust – to withstand distribution shifts, rare (“black swan”) events, and adversarial attacks . While broader robustness against perceptible or unforeseen attacks remains nascent, even the limited $l_p$-type adversarial robustness in vision (small perturbations of inputs subject to a small p-norm constraint) and language models (restricted to limited tokens for language models) is unsolved and the mechanisms of these vulnerabilities are poorly understood.

Adversarial robustness can serve as a testbed and proxy for broader safety challenges: If we can’t defend against small, crafted perturbations, we’re unlikely to solve more complex safety issues. Despite extensive research, we lack a comprehensive understanding of AI vulnerabilities . Competing theories explain adversarial examples: non-robust features , feature superposition , decision boundary tilting , insufficient regularization , and neural tangent kernel perspectives . Each offers insights, but a unified understanding remains elusive.

Interpretability, the interdisciplinary effort to understand AI systems , is critical for AI safety . It encompasses a spectrum of approaches, from feature attribution methods to concept-based explanations . Recent research reveals intriguing connections between interpretability and adversarial robustness . Adversarially trained models often exhibit improved interpretability , while more interpretable models tend to be more robust . This symbiosis suggests that advances in one area could yield benefits in the other, potentially leading to AI systems that are both more transparent and secure .

Mechanistic interpretability, a novel bottom-up approach, aims to reverse-engineer neural networks’ computational mechanisms . By treating networks as computational graphs, it seeks to uncover circuits responsible for specific behaviors . This approach offers a path to understanding AI’s internal cognition at a granular level, potentially revealing how adversarial vulnerabilities arise and propagate through networks . Techniques such as activation patching and circuit analysis provide powerful tools for dissecting model behavior. However, challenges remain in scaling these methods to large language models and ensuring their reliability in the face of potential “interpretability illusions” . Despite these hurdles, mechanistic interpretability shows promise in bridging the gap between theoretical understanding of model behavior and practical techniques for enhancing robustness and safety .

We argue that the intersection of adversarial robustness and mechanistic interpretability could significantly advance AI safety research. This proposal explores synergies between these fields to develop safer AI systems, structured as follows: We review theories of adversarial vulnerability (Section 2.1), examine connections between robustness and interpretability (Section 2.2), overview mechanistic interpretability concepts and techniques (Section 2.3), identify key open questions and future directions (Section 2.4). Based on this foundation, we propose a research agenda (Section 3), outline experiments (Sections 4), and finally, consider potential impacts and ethical considerations (Section 5). By pursuing this agenda, we aim to contribute to the development of powerful, transparent, and reliable AI systems aligned with human values, advancing AI safety as capabilities continue to grow rapidly.

Background

Theories of Adversarial Vulnerability

Despite extensive research, the underlying causes of adversarial vulnerability in neural networks remain contested. Several theories attempt to explain this phenomenon, each offering unique insights and implications for interpretability:

Non-robust features hypothesis. Proposed by , this theory argues that adversarial examples exploit highly predictive but imperceptible features in the data. It is supported by the ability of models trained on adversarially perturbed datasets to generalize to clean data and the transferability of adversarial examples between models . However, precisely characterizing these non-robust features remains challenging.

Superposition hypothesis. In contrast, link adversarial vulnerability to the phenomenon of feature superposition in neural networks. This theory suggests that higher degrees of superposition correlate with increased susceptibility to attacks, explaining the observed robustness-accuracy trade-off . Recent work by extends this concept to more complex architectures, uncovering both isotropic and anisotropic superposition in grid world decision transformers.

Boundary tilting hypothesis. take a geometric perspective, proposing that adversarial examples arise from the high-dimensional nature of data manifolds. This theory suggests that adversarial vulnerability might be an inherent property of high-dimensional classification tasks, explaining the existence of adversarial examples even in simple, linear classifiers.

Insufficient regularization hypothesis. Some researchers argue that insufficient regularization during training is the root cause of adversarial vulnerability . This view is supported by the success of adversarial training in improving robustness . Recent work has proposed refined methods to address the robustness-accuracy trade-off , though the relationship between standard regularization and adversarial robustness remains unclear.

Neural tangent kernel perspective. A more recent perspective, based on the neural tangent kernel (NTK) , connects adversarial vulnerability to the properties of the NTK. This theory suggests that adversarial examples exploit features corresponding to the largest NTK eigenvalues, which are learned early in training but may not align with human-interpretable features.

These theories, while distinct, are not mutually exclusive. The non-robust features and superposition hypotheses both highlight the importance of feature representations in adversarial vulnerability, albeit from different angles. The boundary tilting and NTK perspectives offer complementary geometric and analytical frameworks for understanding the phenomenon. The insufficient regularization hypothesis, meanwhile, focuses on the training process itself, potentially encompassing aspects of the other theories.

From an interpretability standpoint, each theory suggests different approaches. The non-robust features hypothesis calls for methods to identify and visualize these features. Understanding superposition could lead to more interpretable and robust models. The boundary tilting and NTK perspectives highlight the need for techniques to visualize and analyze high-dimensional decision boundaries and kernel properties. Finally, the regularization perspective suggests that improving interpretability might itself serve as a form of regularization, potentially enhancing robustness.

As the field progresses, a unified theory that integrates these perspectives remains a key goal. Such a theory would need to account for architectural differences in robustness , extend to domains beyond image classification , and scale to large language models . Moreover, aligning these theories with human perception and reasoning presents an ongoing challenge in the pursuit of truly robust and interpretable AI systems.

Connections between Robustness and Interpretability

Recent research reveals a deep connection between adversarial robustness and model interpretability :

Interpretability Enhancing Robustness. Techniques that improve model interpretability often lead to increased adversarial robustness. A key example is input gradient regularization, which has been shown to simultaneously improve the interpretability of saliency maps and enhance adversarial robustness . Additionally, techniques like lateral inhibition and second-order optimization have been found to improve both interpretability and robustness concurrently.

Robustness Improving Interpretability. Conversely, methods designed to improve adversarial robustness often lead to more interpretable models. demonstrated that adversarially trained classifiers exhibit improved interpretability-related properties, including more human-aligned feature visualizations. showed that robust models produce better representations for transfer learning tasks. Furthermore, adversarially trained networks yield improved representations for image generation and modeling the human visual system , suggesting that robustness leads to simpler, more interpretable internal representations.

Designing Adversaries via Interpretability Tools. Interpretability techniques can be leveraged to design more effective adversarial attacks, which in turn can be used to validate interpretability methods. , , , and have demonstrated how interpretability insights can guide the creation of targeted adversarial examples. This approach not only helps in understanding model vulnerabilities but also serves as a rigorous way to demonstrate the usefulness of interpretability tools.

Adversarial Examples Aiding Interpretability. Adversarial examples themselves can serve as powerful tools for model interpretation. They have been particularly useful in trojan detection methods . Beyond trojan detection, adversarial examples can reveal important features and decision boundaries in neural networks , enhancing our mechanistic understanding of model behavior.

These interconnections between robustness and interpretability – while the underlying mechanisms remain poorly understood – suggest that advances in one area could benefit the other, potentially leading to AI systems that are both more secure and more transparent.

Fundamentals of Mechanistic Interpretability

Mechanistic interpretability aims to reverse-engineer the computational mechanisms of neural networks, providing a granular, causal understanding of AI decision-making . This approach treats neural networks as computational graphs, uncovering circuits responsible for specific behaviors . It offers a promising path to address the challenges identified in adversarial vulnerability theories and bridge the gap between robustness and interpretability.

Core concepts include features as fundamental units of representation, circuits as computational primitives, and motifs as universal patterns across models/tasks . These concepts provide a framework for understanding how models process information and make decisions, potentially explaining what decides if a neural network mechanism is robust or vulnerable. For a visual explanation of the core concepts, refer to .

Key techniques span observational and interventional methods. Observational approaches include (structured) probing , the logit lens , and sparse autoencoders . Interventional methods, such as activation patching (also called causal tracing or interchange interventions ), allow for direct manipulation of model internals. These techniques can be used to study how adversarial examples affect model behavior at a mechanistic level. Furthermore, circuit analysis techniques to localize and understand subgraphs responsible for specific behaviors can be partially automated . Causal abstraction and causal scrubbing provide rigorous frameworks for hypothesis testing, potentially offering new ways to validate theories of adversarial vulnerability.

Mechanistic interpretability has achieved notable successes, including the identification of specific circuits in vision and language models , the discovery of universal motifs across different architectures , and the development of techniques for targeted interventions in model behavior . However, as a young and pre-paradigmatic field, significant challenges remain. Scalability issues persist when applying current techniques to larger models , and achieving a comprehensive understanding of complex neural networks remains elusive . The reliability of mechanistic insights is further challenged by the potential for interpretability illusions and the complex dynamics of models embedded in rich, interactive environments . These environmental interactions introduce two critical challenges: externally, models may adapt to and reshape their environments through in-context learning , and internally, they may exhibit the hydra effect, flexibly reorganizing their representations to maintain capabilities even after circuit ablations .

Despite these limitations, mechanistic interpretability offers a powerful toolkit for exploring the intersection of adversarial robustness and model interpretability. Providing a causal understanding of model behavior may help reconcile the seemingly contradictory theories of adversarial vulnerability. For a detailed review of the field, refer to .

Open Questions and Future Directions

The intersection of adversarial robustness and mechanistic interpretability presents several critical challenges and opportunities for future research.

Joint Challenges.

1. Both fields ultimately aim to align model behavior with human expectations, albeit through different approaches. Mechanistic interpretability aims to identify circuits corresponding to human-understandable concepts. Similarly, adversarial robustness makes models robust to perturbations that humans would consider insignificant. Developing human-aligned evaluation metrics for both interpretability and robustness remains a challenge .
   • How can we define and measure “human-aligned robustness” beyond simple $l_p$-norm constraints?
   • Can mechanistic interpretability help us understand why certain perturbations are perceived as adversarial by humans while others are not?
2. Both fields grapple with scaling to larger models. Mechanistic interpretability techniques could potentially be used to detect and understand trojans or backdoors in large language models, a form of adversarial vulnerability . Understanding how concepts like adversarial examples and robustness translate to large language models is an active area of research .
   • How do the relationships between interpretability and robustness scale to large language models?
   • Can mechanistic interpretability techniques help us understand and mitigate vulnerabilities in large language models, such as prompt injection attacks?

Understanding. Can we develop a unified theoretical framework that explains the observed connections between the robustness and interpretability of neural networks?

• a) How can we reconcile theories of non-robust features , superposition , and boundary tilting into a cohesive explanation of adversarial vulnerability?
• b) What is the causal relationship between robustness and interpretability? Does one directly lead to the other, or is there a common underlying factor? Can we quantify the degree of interpretability improvement in robust models across different architectures and tasks?
• c) How does feature representation, particularly superposition, relate to adversarial vulnerability and interpretability? How can we measure and control the degree of superposition in large-scale models to improve both interpretability and robustness? Is there a fundamental trade-off between representational capacity (enabled by superposition) and robustness?
• d) Can mechanistic interpretability help us understand why certain perturbations are perceived as adversarial by humans while others are not?

Engineering. Can insights from mechanistic interpretability be leveraged to design inherently more robust architectures or training procedures? And vice versa, how can we use adversarial examples to aid mechanistic interpretability?

• a) Can we use mechanistic interpretability to predict which model components will most likely be exploited by adversarial attacks? How can we leverage insights about model circuits to design training procedures or architectures that are inherently more robust?
• b) How can we systematically use adversarial examples to probe and understand the decision-making processes of complex models? Can we develop adversarial attacks targeting hypothesized computational circuits within models ?
• c) Can mechanistic interpretability insights help us understand and mitigate other types of vulnerabilities in AI systems, such as backdoors or trojans ?

Priority should be given to understanding mechanisms first, followed by improving robustness and interpretability on benchmarks second.

Research Objectives and Methodology

This project aims to synergize mechanistic interpretability and adversarial robustness to develop safer AI systems. Our objectives are:

1. Investigate feature superposition’s role in interpretability and robustness .
2. Develop techniques for reverse-engineering robust models, extending beyond vulnerability localization .
3. Create interpretability-guided approaches for identifying and mitigating adversarial vulnerabilities across architectures and tasks.
4. Design training methods leveraging adversarial robustness to improve interpretability .

Our methodology combines mechanistic interpretability, adversarial training, and causal inference:

1. Interpretability Analysis: Apply feature visualization , circuit dissection , and sparse autoencoders to understand internal representations and search for non-robust features .
2. Circuit Identification: Use activation patching and logit attribution to identify critical circuits.
3. Causal Intervention: Validate understanding of model mechanisms and test robustness improvements.
4. Adversarial Sample Generation: Develop targeted adversarial examples exploiting specific vulnerabilities .
5. Robustness-Interpretability Integration: Develop training procedures incorporating adversarial objectives for enhanced interpretability and interpretability constraints for enhanced robustness .

Proposed Experiments

1. Superposition and Adversarial Vulnerability: Investigate correlation between superposition and robustness across architectures.
   • Develop quantitative metrics to measure the degree of feature superposition in model representations.
   • Investigate the correlation between superposition metrics and adversarial robustness across different model architectures.
   • Design and test regularization techniques (e.g. $l_1$ penalty ) to encourage more disentangled representations, assessing their impact on both interpretability and robustness.
2. Reverse Engineering Robust Models: Compare computational structures of robust and non-robust models.
   • Train standard and adversarially robust models on benchmark tasks .
   • Apply circuit analysis techniques to extract symbolic representations of the models’ decision-making processes.
   • Compare the extracted representations between robust and non-robust models to identify key differences in their computational structures.
3. Interpretability-Guided Robustness Improvement: Use mechanistic insights to develop targeted interventions.
   • Use mechanistic interpretability techniques to identify brittle features or circuits that make models vulnerable to adversarial attacks.
   • Develop targeted regularization or architectural modifications based on these insights.
   • Evaluate the effectiveness of these interventions in improving robustness while maintaining or enhancing interpretability.
4. Adversarial Attacks as Interpretability Tools: Design attacks targeting specific circuits to validate hypotheses.
   • Design adversarial attack algorithms that target specific circuits or features identified through mechanistic interpretability.
   • Use these targeted attacks to validate or refute hypotheses about a model’s internal representations.
   • Develop a framework for using adversarial examples to enhance our understanding of model behavior and improve interpretability methods.

Potential Impacts and Ethical Considerations

This research promises several benefits: enhanced AI safety through vulnerability detection , increased model transparency fostering trust and regulatory compliance , improved robustness against distribution shifts and adversarial attacks , and theoretical insights into neural network behavior .

However, it also poses risks: dual-use potential for creating sophisticated attacks , unintended AI capability amplification , “interpretability illusions” leading to false sense of security , and privacy concerns from model information extraction , adversarially validate interpretability methods , and potentially explore privacy-preserving techniques .

To mitigate these risks, we will ethically review all experiments, if necessary, collaborate with AI ethics and security experts, establish responsible vulnerability disclosure protocols, and prioritize defensive techniques over offensive capabilities. Moreover, we will engage regularly with the AI safety community to discuss information hazards.

These measures aim to maximize research benefits while minimizing potential harm to AI safety and ethics.

Conclusion

This project aims to advance AI safety by exploring the synergy between mechanistic interpretability and adversarial robustness. Our interdisciplinary approach combines insights from both fields to develop more transparent, reliable, and human-aligned AI systems. By addressing key challenges in understanding and mitigating vulnerabilities, we hope to contribute significantly to the responsible development and deployment of AI in high-stakes applications and help ensure humanity’s survival and prosperity in the face of superhuman AI.