arXiv:2605.25304v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) have emerged as a cornerstone approach for interpretable machine learning, providing human-understandable intermediate representations through explicit concept activations. However, this interpretability fundamentally introduces a critical, previously unexplored attack surface: the concept bottleneck layer itself. We present a comprehensive, systematic study of concept-level adversarial vulnerabilities in CBMs, revealing that targeted, minimal perturbations operating on input pixels can induce catastrophic misclas
The increased adoption and research into interpretable AI models like CBMs have naturally led to a deeper scrutiny of their vulnerabilities, particularly as they move towards more critical applications.
This research reveals new attack vectors in AI systems designed for interpretability, fundamentally challenging the assumption that transparency equates to security or reliability in complex models.
The focus for secure and reliable AI development must now expand to include the defense of interpretability layers, complicating the deployment of CBMs in high-stakes environments without robust adversarial training.
- · AI robustness and security researchers
- · Adversarial AI specialists
- · Organizations developing secure AI architectures
- · Developers relying solely on interpretability for AI safety
- · Companies deploying CBMs without adversarial defenses
- · Sectors requiring provable AI security (e.g., defense, medical)
Increased research into adversarial robustness for interpretable AI models will be prioritized.
New standards and protocols for the secure design and deployment of interpretable AI will emerge, focusing on defending against concept-level attacks.
This could lead to a re-evaluation of the 'interpretability equals safety' paradigm, potentially shifting towards more holistically secure but perhaps less transparent AI systems in critical applications.
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Read at arXiv cs.LG