Grounded but Misleading: Evaluating Semantic Alignment in AI-Generated Security Explanations
arXiv:2602.05056v2 Announce Type: replace-cross 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,
The proliferation of context-aware social engineering and advanced AI models necessitates immediate advancements in explainable AI for security applications, driving research into semantic alignment.
A strategic reader should care because the inability of AI to accurately explain its security risk assessments creates vulnerabilities in critical systems and undermines trust in autonomous security solutions.
Current methods for AI-generated security explanations are shown to be potentially misleading, shifting the focus towards evaluating the semantic alignment and interpretability of these systems beyond mere lexical grounding.
- · AI security explanation developers
- · Cybersecurity firms
- · Organizations relying on AI for threat detection
- · Scam perpetrators
- · Systems with unverified AI explanation capabilities
Increased research and development into robust, semantically verifiable AI explanation models.
Improved resilience of individuals and organizations against sophisticated AI-powered social engineering attacks.
Enhanced regulatory scrutiny and standards for the transparency and reliability of AI systems in security-critical domains.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL