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This is a Experimental Study study.

October 10, 2025Open Access

Explainable but Vulnerable: Adversarial Attacks on XAI Explanation in Cybersecurity Applications

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Experimental study finds vulnerabilities of XAI methods in cybersecurity applications, highlighting urgent need for resilience.

Adversarial attacks compromise the reliability of explainable AI methods, impacting model decisions significantly.
Fairwashing explanation, manipulation explanation, and backdoor-enabled manipulation are major attack tactics identified.
This research evaluates six different attack procedures on explanation methods like SHAP and LIME in various scenarios.
Urgent attention is required to strengthen the resilience of explainable AI methods against these vulnerabilities.

Mia et al. (2025) studied this question.

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October 10, 2025Open Access

View Full Paper

Ask AI

Bookmark

Authors

Experimental study finds vulnerabilities of XAI methods in cybersecurity applications, highlighting urgent need for resilience.

Adversarial attacks compromise the reliability of explainable AI methods, impacting model decisions significantly.
Fairwashing explanation, manipulation explanation, and backdoor-enabled manipulation are major attack tactics identified.
This research evaluates six different attack procedures on explanation methods like SHAP and LIME in various scenarios.
Urgent attention is required to strengthen the resilience of explainable AI methods against these vulnerabilities.

Mia et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark