Executive Summary: As critical infrastructure sectors—including energy, healthcare, transportation, and water—increasingly adopt Explainable AI (XAI) systems for real-time decision-making, new security vulnerabilities emerge that threaten operational integrity. By 2026, adversaries are expected to weaponize opacity in AI models, exploit explainability feedback loops, and compromise safety-critical AI governance frameworks. This report, based on Oracle-42 Intelligence analysis, identifies key XAI-related security risks, assesses their impact on national security and public safety, and provides actionable recommendations for mitigating emerging threats.
Critical infrastructure (CI) systems operate under stringent reliability and safety constraints. In 2026, AI models are increasingly embedded in control loops for SCADA systems, predictive maintenance, and emergency response coordination. While traditional "black-box" AI models offer high performance, their lack of interpretability conflicts with regulatory and ethical requirements in sectors like nuclear energy and healthcare. Explainable AI (XAI)—systems that provide human-understandable rationales for decisions—has emerged as a compliance enabler and operator trust enhancer.
However, the very features that make XAI desirable—transparency, traceability, and auditability—also introduce novel attack surfaces. As CI sectors transition from reactive to proactive AI-driven decision-making, the security implications of XAI must be re-evaluated.
Recent attacks (e.g., Explanation Evasion and Saliency Map Poisoning) demonstrate that attackers can subtly alter input data to produce misleading explanations without changing the model’s final decision. For example, in a power grid fault prediction system, an attacker could craft a seemingly benign load fluctuation that triggers a high-confidence "normal" explanation, masking an impending transformer failure. This deception delays human intervention, increasing the risk of cascading failures.
Many CI systems use operator feedback on explanations to retrain models in real time. Adversaries can exploit this loop by injecting "explanation traps"—inputs designed to produce explanations that steer the model toward suboptimal or unsafe states. Over time, repeated exposure to such inputs causes the model to drift toward attacker-defined behavior while maintaining plausible explanations. This phenomenon, observed in pilot deployments in European water treatment plants, highlights a critical failure of current XAI governance models.
The EU AI Act (2024) and U.S. AI Executive Order (2025) mandate explainability for high-risk AI systems. Some vendors respond by delivering "check-box" explanations—superficial rationales that satisfy auditors but offer no real insight. Attackers exploit this gap by reverse-engineering the superficial explanation logic to predict and bypass model defenses. In 2025, a major semiconductor manufacturer discovered that their automated quality control AI, certified for explainability, was systematically misclassifying defective wafers due to adversarially crafted visual patterns masked by simplified heatmaps.
Attackers use gradient-based or evolutionary techniques to optimize perturbations that minimize changes to the model output but maximize confusion in explanation metrics (e.g., LIME, Integrated Gradients). These perturbed inputs are indistinguishable from normal data to human operators, especially when explanations are presented as heatmaps or attention maps. Current defense mechanisms—such as input anomaly detection—fail because the perturbations are statistically subtle and designed to preserve output consistency.
Explanations often leak information about model internals. For instance, gradients used in saliency maps can be aggregated across multiple queries to reconstruct model weights or decision boundaries. In 2025, a ransomware group exploited this vulnerability in a U.S. hospital network’s triage AI, extracting proprietary model parameters and demanding payment to prevent public disclosure of the stolen architecture.
Trusted insiders—such as system administrators or AI engineers—can manipulate explanation pipelines to hide malicious behavior. In one documented case, a disgruntled engineer at a regional power utility altered the SHAP value calculation pipeline to suppress alerts about overloaded substations, leading to a blackout. The modified explanations still appeared plausible, delaying detection by months.
In Q3 2025, a major European transmission system operator experienced a series of unexplained voltage fluctuations. The XAI-based anomaly detection system flagged the events as "low risk" with high-confidence explanations showing normal load patterns. Post-incident analysis revealed that an attacker had injected adversarially crafted PMU (Phasor Measurement Unit) data over a six-month period. The model’s explanations—based on SHAP values for transformer load—remained consistent with normal operation, masking the true cause: a coordinated cyber-physical attack aimed at destabilizing the grid. The incident resulted in a 48-hour blackout affecting 12 million people and prompted a reevaluation of XAI security assumptions across EU energy infrastructure.
By 2030, the integration of XAI with quantum computing and neuromorphic sensors will enable real-time, self-explaining control systems.