Imagine a vast network, a digital web of interconnected points, holding valuable, hidden connections. Now, picture an attacker, armed not with brute force, but with artificial intelligence, subtly probing this network to uncover its secrets. This isn’t science fiction, but the emerging threat of link stealing attacks, powered by large language models (LLMs). Graph Neural Networks (GNNs) excel at analyzing these complex networks, crucial for applications like social media analysis and drug discovery. But this power comes with a vulnerability. GNNs, trained on sensitive link information, can be targeted by attackers seeking to expose these connections. Traditional link stealing attacks rely on exploiting the GNN's predictions, but a new approach leverages the power of LLMs. By combining the GNN’s output with the rich textual features of nodes (like paper titles and abstracts in a citation network), LLMs can extract link information with alarming accuracy. Researchers have demonstrated this threat by crafting clever prompts that guide LLMs to infer links between nodes, even in “black-box” scenarios where the attacker has limited knowledge of the GNN itself. This novel attack method bypasses previous limitations by leveraging LLMs' ability to process varying data dimensions, meaning a single LLM can attack multiple datasets. The implications are significant. From revealing private relationships in social networks to uncovering proprietary information in business data, LLM-powered link stealing poses a serious privacy risk. While this research highlights a vulnerability, it also paves the way for developing stronger defenses. Understanding how LLMs exploit these weaknesses is crucial for building more robust GNNs that can withstand these sophisticated attacks. As AI becomes more integral to network analysis, safeguarding against these evolving threats will be paramount.