How Vulnerable Is Your AI to Hidden Attacks?

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Author: Denis Avetisyan

New research offers a way to measure the risk of adversarial attacks that transfer from unseen sources, even without knowing the attacker’s methods.

Each model possesses a unique adversarial subspace, yet these subspaces inevitably overlap, demonstrating that even disparate systems share vulnerabilities which will, in time, become points of systemic failure.
Each model possesses a unique adversarial subspace, yet these subspaces inevitably overlap, demonstrating that even disparate systems share vulnerabilities which will, in time, become points of systemic failure.

This study introduces a practical framework leveraging surrogate models and Centered Kernel Alignment to quantify the resilience of neural networks to transferred black-box adversarial attacks.

Despite the increasing reliance on neural networks in critical applications, comprehensively evaluating their vulnerability to adversarial attacks remains a significant challenge. This paper, ‘Quantifying the Risk of Transferred Black Box Attacks’, addresses this gap by proposing a practical framework for resilience testing against transferred, black-box adversarial attacks—a particularly insidious threat due to their high transferability. The core contribution lies in strategically employing surrogate models, selected via Centered Kernel Alignment (CKA) similarity, to efficiently map adversarial risk when exhaustive testing is computationally infeasible. Can this approach provide organizations with actionable insights to proactively mitigate the evolving threat landscape of adversarial machine learning?

The Inevitable Distortion: Adversarial Vulnerabilities

Despite advancements in machine learning, models remain vulnerable to subtle, intentionally crafted input perturbations—adversarial attacks. These attacks modify input data imperceptibly to humans, yet cause incorrect outputs with high confidence. This persistence highlights a fundamental limitation: a reliance on correlation rather than genuine understanding. The threat is amplified by ‘black box attacks’ requiring no prior model knowledge, expanding the attack surface. The ability to consistently deceive models suggests they are not robust indicators of reality, but systems susceptible to the inevitable erosion caused by time and manipulation.

Standardizing Resilience: RobustBench and AutoAttack

RobustBench provides a standardized platform for evaluating adversarial robustness in image classification, addressing inconsistencies in the field. Its key component, AutoAttack, is an ensemble of adaptive attacks—Square Attack, APGD, and FAB—designed to rigorously probe model vulnerabilities. By combining these attacks, RobustBench reveals vulnerabilities missed by simpler evaluations, often stemming from overfitting or brittle defense mechanisms. This comprehensive, ensemble-based evaluation is crucial for discerning genuine resilience.

Quantifying and Mitigating Risk: A Matter of Degrees

Effective risk quantification requires considering both the allowable input change (perturbation budget) and the likelihood of adversarial examples in deployment. Current methods often underestimate risk by focusing solely on expected inputs. A proposed resilience testing framework uses surrogate models to approximate model behavior under perturbation, revealing CKA similarity scores ranging from 0.32 to 0.57 (median ~0.45). Highly similar models are defined by a CKA threshold of 0.55, while low-similarity models fall below 0.35. Analysis reveals an adversarial subspace dimensionality of ~25. While adversarial training improves robustness, it doesn’t eliminate risk, and preprocessing techniques offer limited, often trade-off-laden, protection.

Toward Comprehensive Security: Embracing Full Coverage

‘Full-Coverage Testing’ represents a rigorous evaluation methodology assessing resilience against all potential adversarial attacks, paramount for safety-critical applications. Employing ‘image statistics detectors’ and ‘neuron activation detectors’ introduces proactive defense layers, identifying malicious inputs before they compromise the core model. Diagonal Box Similarity (DBS) quantifies coverage of adversarial subspaces, typically ranging from 0.4 to 0.75. These strategies—robust evaluation, preemptive detection, and resilient training—are fundamental to creating AI systems that inspire confidence. The pursuit of comprehensive security isn’t merely fortification, but ensuring these systems age gracefully, retaining utility as threats evolve.

The pursuit of resilience, as detailed in the study of transferred black-box attacks, mirrors a fundamental principle of all systems: inevitable decay. This work attempts to model and quantify that decay, not to prevent it – exhaustive testing being an impossibility – but to understand its trajectory. As Carl Friedrich Gauss observed, “If I have to explain it to you, I’m not qualified to do so.” The paper’s methodology, leveraging CKA similarity to predict attack transferability, suggests a sophisticated understanding of systemic relationships. It acknowledges the inherent latency in assessing risk – the ‘tax every request must pay’ – and proposes a practical method to navigate that delay, accepting that stability is, indeed, an illusion cached by time. The strategic use of surrogate models isn’t about achieving perfect foresight, but gracefully accommodating the inevitable entropy of complex systems.

What’s Next?

The presented work offers a pragmatic assessment of risk—a momentary stay against the inevitable entropy of any predictive system. Logging the transferability of adversarial examples via CKA similarity is, in essence, the system’s chronicle; a record of vulnerabilities as they manifest across a landscape of surrogate models. However, the selection of those surrogates remains a critical, and inherently limited, endeavor. Each chosen model is merely a snapshot in a vast parameter space, offering a partial view of the complete threat profile. Deployment is a moment on the timeline, and the true measure of resilience will be determined by the system’s adaptation to attacks not yet conceived.

Future investigations should address the dynamic nature of this risk. Static assessments, while valuable, fail to account for the adversarial’s own evolution—the continual refinement of attack strategies. Exploring methods to actively probe the decision boundary—to deliberately introduce perturbations and observe the system’s response—may reveal vulnerabilities hidden by passive analysis. The challenge lies not in eliminating risk—that is an asymptotic ideal—but in gracefully accommodating its presence, building systems that degrade predictably rather than collapse catastrophically.

Ultimately, the quantification of transferred black-box attacks serves as a reminder: predictive models are not immutable truths, but fragile constructs. Their longevity is not guaranteed by complexity, but by a continuous cycle of assessment, adaptation, and acceptance of inherent limitations. The chronicle will continue, and each logged vulnerability will mark another step in the system’s inevitable, yet hopefully elegant, decay.

Original article: https://arxiv.org/pdf/2511.05102.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-10 18:07