Journal of Intelligent Computing Pit Pichappan 17 4 2026 https://doi.org/10.6025/jic/2026/17/4/179-199 https://www.dline.info/jic/fulltext/v17n4/jicv17n4_1.pdf Large language models (LLMs) frequently generate plausible but factually inaccurate outputs, commonly known as hallucinations. Moving beyond treating hallucination as a simple binary factuality problem, this study evaluates it as a multidimensional risk encompassing generation frequency, confidence accuracy mismatch, detectability, and downstream trust impact. Analyzing a comprehensive dataset of 10,000 interactions across six frontier AI models and platforms, we assessed hallucination prevalence, epistemic calibration, and human consequences. Results reveal distinct variations in hallucination rates, with Llama and Mistral exhibiting the highest frequencies. Crucially, we identified a pronounced confidence accuracy mismatch across systems; notably, Mistral presented the most concerning risk configuration by combining high hallucination rates, maximum AI confidence, and the lowest detection rates. Conversely, GPT-5 demonstrated the most favorable factual accuracy profile. Cross-tool analysis demonstrated that hallucination occurrence, rather than specific tool identity, serves as the primary driver of severe trust erosion. Specifically, the mere presence of an error caused a massive trust penalty that subsequent detection alone could not resolve. These findings proved that AI hallucination is a systemic, cross-platform reliability challenge. Mitigating its impact requires integrated strategies addressing confidence calibration, proactive detection mechanisms, and user interface designs that foster appropriate skepticism, rather than relying solely on reducing raw factual error rates.