Adversarial Robustness in XAI Specifications: Why Explainability Must Be Secure

May 6, 2026Open Access

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Key Points

This research aims to explore the necessity of secure specifications in explainable AI to enhance adversarial robustness.
Analyzed various explainable AI models for vulnerabilities to adversarial attacks.
Developed criteria for assessing the security of XAI specifications.
Conducted experiments to evaluate the robustness of different models against adversarial inputs.
Identified key vulnerabilities in popular explainable AI frameworks.
Demonstrated that secure specifications significantly improve adversarial robustness.
Provided quantifiable metrics showing increased resilience against manipulation.

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Demonstrates the importance of secure explainability in AI systems, indicating potential vulnerabilities.

Research article: Adversarial Robustness in XAI Specifications: Why Explainability Must Be Secure

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Oleh Ivchenko (2026) studied this question.

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