Why Fake Cititations are becoming the Biggest Trust Problem in Legal AI

On July 2, 2026, the Supreme Court of India delivered one of the clearest warnings yet about the risks of using artificial intelligence in legal work without verification.

The Court set aside orders passed by the National Company Law Tribunal and the National Company Law Appellate Tribunal after finding that the decisions had relied on non-existent and hallucinated judicial precedents.

The matter arose from insolvency proceedings involving Essel Infraprojects Ltd. The Supreme Court held that a judicial decision influenced by fabricated legal material could not be treated as a valid determination, regardless of whether that material had a direct or indirect bearing on the outcome. It called for zero tolerance towards the use of unverified AI-generated precedents and directed the Bar Council of India to consider appropriate safeguards and disciplinary norms.

That development should worry every lawyer, judge, researcher, law student, and legal technology company. Not because AI is useless. It should worry us because AI is becoming useful enough to be trusted too quickly.

The legal profession is now standing at a strange point. On one hand, large language models can read, summarise, compare, explain, draft, and organise information at a speed no human can match. They can help lawyers prepare faster, think more broadly, and reduce hours of repetitive work. On the other hand, the same systems can produce answers that sound legally correct while being unsupported by real law. They can invent cases, misstate precedents, or present outdated authorities as if they are still valid. The central question, therefore, is not whether AI can assist lawyers. It clearly can. The real question is whether the legal information beneath the answer can be trusted.

To understand why fake citations appear, it is important to understand what a large language model is designed to do. An LLM does not approach a legal question in the same way a lawyer approaches a legal database. It does not begin by identifying the relevant jurisdiction, locating binding authorities, checking their subsequent treatment, and then forming a conclusion. Its primary task is to generate a coherent response based on patterns in the information it has processed.

That is also where another challenge arises. Large language models have access to enormous volumes of information, including articles, blogs, commentaries, newsletters, summaries, and other secondary sources discussing judicial decisions. The same judgment may be interpreted, paraphrased, or simplified hundreds of times by different authors. As those interpretations are repeated, small inaccuracies can gradually alter the way the decision is understood, even when the original judgment says something more nuanced. When responding to a legal query, an LLM may rely on or direct the user to these secondary sources rather than the judgment itself, effectively treating another person’s interpretation as the underlying authority. In law, however, the judgment, not its interpretation, is the primary source. This means that the problem is not only having too little information. Having too much unchecked and unverified information can be equally problematic.

That is why a model can produce a citation that looks convincing without confirming whether the case exists. It may understand the format of a legal citation and the kind of authority that would support a proposition. From those patterns, it can construct a case name, court, year, paragraph number, or even a link. This is what makes fake citations especially dangerous. They often appear alongside accurate legal language, familiar principles, and professional formatting, which creates an impression of reliability.

The same risk does not disappear merely because a product is presented as a legal AI tool. A system may be designed specifically for lawyers and still produce unreliable results if it is built on incomplete, poorly structured, or weakly verified legal data. If the underlying repository is limited, outdated, or disconnected from the wider body of case law, the quality of the answer will eventually reflect those gaps.

This is where the difference between a legal database and a collection of legal documents becomes important. A useful legal research system must do more than store judgments. It must show how those judgments relate to one another, whether they remain good law, what precedential value they carry, which paragraphs matter, and how later courts have treated them. None of this is possible without a robust citator. A citator maps the life of a judgment by showing where it has been followed, distinguished, criticised, overruled, or otherwise considered by later courts.

Building that kind of legal infrastructure takes years. Judgments must be collected, cleaned, classified, linked, and continuously updated. Citation histories and subsequent treatment must be tracked so that lawyers can understand how an authority has evolved and whether it can still be relied upon.

The fake citation problem is broader than the invention of cases. A citation may refer to a real judgment and still lead a lawyer in the wrong direction. The case may concern a different statutory framework. The quoted passage may be obiter rather than part of the binding reasoning. A later bench may have questioned the principle. A higher court may have reversed the decision. The judgment may be persuasive in one forum but carry little or no precedential weight in another.

The quality of legal AI therefore depends on more than whether it can find a document. It depends on whether it can place that document within the structure of the law.

Retrieval can reduce hallucinations by giving the model access to external material, but retrieval alone does not guarantee that the material found is complete, current, or legally applicable. A system may retrieve a judgment because it contains similar words while missing the binding decision that controls the issue. It may also locate a passage without recognising that it was later disapproved or superseded.

For many ordinary tasks, this level of legal infrastructure may not be necessary. General-purpose LLMs can be highly effective when the task is primarily transactional or linguistic. They can organise facts, summarise documents, simplify complex language, compare clauses, prepare a first draft, extract dates, create a chronology, or help structure an argument.

The nature of the task changes, however, when the output depends on legal authority. If a lawyer is preparing a pleading, advising a client on the state of the law, evaluating a cause of action, building a litigation strategy, or placing a precedent before a court, the answer must be grounded in verified and current legal material. At that stage, language capability alone is insufficient. The system needs access to citation-grade legal data and the tools required to understand how that data is connected.

This is particularly important because the responsibility for the final work remains with the legal professional. Courts may permit responsible use of AI, but they will not accept the software as an excuse for placing fabricated or misleading authorities on record. Verification is not an optional technical step. It is part of professional responsibility.

The lesson is not that the legal industry should step away from AI. It is that legal AI must be held to a higher standard. A useful legal system should combine the reasoning and language capabilities of modern AI with the far more fundamental requirement of a structured repository of verified legal material. Only then can it connect answers to judgments, judgments to later treatment, propositions to relevant paragraphs, and individual authorities to the wider development of the law.

That principle also underlies CaseMine’s approach to legal AI.

CaseMine has spent years building a legal research ecosystem around judgments, citations, precedents, important paragraphs, and the relationships between authorities. Its citator allows users to examine the history and treatment of cases, while its research tools help identify conceptually relevant judgments and the passages that matter. AMICUS adds conversational AI, document analysis, drafting, and research capabilities on top of that legal foundation.

That depth is what turns legal AI from a fluent interface into a reliable research system.

As AI becomes more fluent, the risk is that users will begin to confuse confidence with correctness. A case name that looks real is not enough. A working link is not enough. A coherent argument is not enough. The authority must exist, it must support the proposition, and it must remain capable of being relied upon.

The future of legal AI will not be determined only by which system produces the fastest answer. It will be determined by which system allows that answer to be examined, traced, and trusted. Fake citations are therefore more than a temporary flaw in a new technology. They expose the central question confronting the entire legal AI industry.