Is this Citation on Point?

In 2023, a New York judge sanctioned two attorneys in Mata v. Avianca for filing a brief with hallucinated citations generated by Chat-GPT. Such failures are largely caught by database lookups; the harder problem is detecting citations that point to real cases but do not support the propositions for which they are offered—a failure mode that existing evaluations of LLMs for legal use cases largely overlook. In this paper, we study proposition-level citation support verification through controlled perturbations of real legal citations obtained from two legal corpora, either replacing the cited case or changing only the pin-point page within the same case. We evaluate fourteen language models on the resulting examples. Models catch 93–100% of wrong-case corruptions. They catch only 37–61% of wrong-pinpoint corruptions on court opinions, and 52–83% on briefs. When models fail to catch wrong-pinpoint corruptions, they accept the citation based on topical overlap rather than page-level support. Scale and extended reasoning narrow the gap but do not close it: GPT-5.4 with high reasoning effort still misses 40% of pinpoint mismatches on court opinions and 18% on briefs. Prompting the model to verify support at the cited page raises recall, but it also raises false positives. Recognizing the right legal topic and verifying support for the cited proposition are distinct capabilities, and current models conflate them.