Grounded but Misleading: Evaluating Semantic Alignment in AI-Generated Security Explanations

Abstract

Online scams increasingly leverage fluent and context-aware social-engineering strategies, creating growing demand for AI systems that explain why a message may be risky. However, explanations that cite detector-derived evidence may still semantically weaken or redirect the intended risk interpretation. We introduce VEXA (Verifying Semantic EXplanation Alignment), a controlled testbed for studying the gap between lexical grounding and semantic risk alignment in AI-generated scam-risk explanations. VEXA generates ungrounded, risk-aligned, and risk-diluting explanations by independently controlling evidence grounding and semantic framing. Across LLM-as-a-judge and human evaluations, explanations can remain comparatively grounded even when their interpretations weaken the detector’s intended risk assessment. In human evaluation, risk-diluting XAI-grounded explanations retained comparatively elevated Perceived Evidence Grounding scores (3.66 ± 1.02) despite lower Helpfulness (3.00 ± 1.41) and Reasoning Support (3.14 ± 1.05) scores. These findings provide controlled evidence of grounding-illusion effects and suggest that trustworthy explanation evaluation must verify not only whether evidence is cited, but also how it is interpreted.