Large language models (LLMs) have been enormously successful in natural language understanding and generation yet their resistance to adversarial manipulation is a significant area of concern. This article is comprised of the methodological empirical study of the LLM strength in the context of the contextual adversarial attacks, which exploit the semantic dependencies and promote framing without altering the syntactic structure. There were four contextual adversarial methods that were created and applied to a range of language understanding and reasoning tasks, which included misleading instructions, semantic obfuscation, contextual distraction and role manipulation. The performance degradation, consistency of responses and the qualitative error severity were the measures of the robustness. The results depicted that there was a significant loss of accuracy and instability of answers in the adversarial environments, and there was the gravest impairment in the attacks of the misleading guidance and role manipulation. Hallucinations and logical inconsistencies were determined to be the significant failure modes, a fact that suggested an inherent defect in contextual reasoning. The findings reveal that the high baseline performance is not an indicator of robustness and introduces the significance of the robustness-aware assessment systems and specially designed defense strategies to ensure the consistent functioning of the LLMs in the real-life setting.