This paper critically evaluates the inadequacies of current Large Language Model (LLM) benchmarks, highlighting systemic gaps in their ability to comprehensively assess LLM capabilities and risks across technological, processual, and human dimensions. The authors analyze 23 state-of-the-art LLM benchmarks, identifying common shortcomings such as cultural bias, limited reasoning assessment, inconsistent implementation, prompt engineering complexity, evaluator diversity, and the neglect of cultural and ideological norms. These issues undermine the reliability and fairness of benchmarking practices, which are essential for evaluating LLMs in real-world applications. The study emphasizes the need for standardized methodologies, regulatory certainties, and ethical guidelines in AI evaluation, advocating for a shift from static benchmarks to dynamic behavioral profiling to better capture LLMs' complex behaviors and potential risks. The authors propose a unified evaluation framework rooted in cybersecurity principles—people, process, and technology—to assess both functionality and security in LLM benchmarks. This framework aims to provide a more holistic evaluation of LLMs, addressing their broader implications and societal impacts. The paper also discusses the challenges in benchmarking, including response variability, difficulty in distinguishing genuine reasoning from technical optimization, tension between helpfulness and harmlessness, linguistic variability, benchmark installation and scalability, biases in LLM-generated evaluations, inconsistent implementation, slow test iteration time, and challenges in proper prompt engineering. These issues highlight the need for more comprehensive and sophisticated benchmarking practices that account for the complexities of modern LLMs and their diverse roles in society and technology. The study concludes that current benchmarking practices are insufficient and that a paradigm shift is necessary to develop universally accepted benchmarks that ensure the safe and effective integration of AI into society.This paper critically evaluates the inadequacies of current Large Language Model (LLM) benchmarks, highlighting systemic gaps in their ability to comprehensively assess LLM capabilities and risks across technological, processual, and human dimensions. The authors analyze 23 state-of-the-art LLM benchmarks, identifying common shortcomings such as cultural bias, limited reasoning assessment, inconsistent implementation, prompt engineering complexity, evaluator diversity, and the neglect of cultural and ideological norms. These issues undermine the reliability and fairness of benchmarking practices, which are essential for evaluating LLMs in real-world applications. The study emphasizes the need for standardized methodologies, regulatory certainties, and ethical guidelines in AI evaluation, advocating for a shift from static benchmarks to dynamic behavioral profiling to better capture LLMs' complex behaviors and potential risks. The authors propose a unified evaluation framework rooted in cybersecurity principles—people, process, and technology—to assess both functionality and security in LLM benchmarks. This framework aims to provide a more holistic evaluation of LLMs, addressing their broader implications and societal impacts. The paper also discusses the challenges in benchmarking, including response variability, difficulty in distinguishing genuine reasoning from technical optimization, tension between helpfulness and harmlessness, linguistic variability, benchmark installation and scalability, biases in LLM-generated evaluations, inconsistent implementation, slow test iteration time, and challenges in proper prompt engineering. These issues highlight the need for more comprehensive and sophisticated benchmarking practices that account for the complexities of modern LLMs and their diverse roles in society and technology. The study concludes that current benchmarking practices are insufficient and that a paradigm shift is necessary to develop universally accepted benchmarks that ensure the safe and effective integration of AI into society.