Large Language Models (LLMs) have demonstrated an impressive ability to retrieve and summarize complex information, but their reliability under conflicting contexts remains poorly understood. We introduce an adversarial extension of the Needle-in-a-Haystack framework where three mutually exclusive “needles” are embedded into long documents. By systematically manipulating factors such as position, repetition, layout, and domain relevance, we evaluate how LLMs handle contradictions. We find that models almost always fail to signal uncertainty and instead confidently select a single alternative, exhibiting strong and consistent biases toward repetition, recency, and specific surface form. We further analyze if these patterns are shared within a model family and size, as well as perform both probability-based and generation-based retrieval. Our framework highlights critical limitations in current LLMs’ robustness to contradiction, revealing potential shortcomings in RAG systems' ability to handle noisy or manipulated inputs, and pose challenges for deployment in high-stakes applications.