AI Hallucinations in the Legal Sector: What They Are, How Often They Occur, and How to Protect Yourself

AI Hallucinations in the Legal Sector: What They Are, How Often They Occur, and How to Protect Yourself

In February 2026, the Superior Court of Justice of the Canary Islands sanctioned a lawyer for citing non‑existent case law in a court filing. The judgments had been generated by a general‑purpose AI, and no one verified them. A few weeks later, a judge in Ceuta was reported for including alleged Supreme Court rulings that also did not exist.

These are not isolated incidents. The public database maintained by Damien Charlotin (a professor at HEC Paris who tracks these incidents worldwide) already counts more than 190 documented cases, with over 30 % occurring in the last quarter.

If your firm has started integrating AI into legal processes (or if professionals are using it on their own), this is a point you cannot ignore.

What an AI Hallucination Is and Why It Happens

Large language models (ChatGPT, Gemini, Claude, etc.) do not retrieve information; they predict the next most likely word given the context. When they lack the exact answer, they do not leave a gap. Instead, they fabricate a plausible response, complete with citations, case numbers, and dates. That fabricated output is a hallucination.

The problem is that it doesn’t sound like an error. The text is articulate, extensive, and seemingly well‑founded. Former Google executive Amr Awadallah described it as “simulated competence.” For a lawyer, detecting a hallucination is not spotting an obvious typo; it is spotting a well‑written argument that turns out to be false.

The more complex or specific the query, the higher the probability of a hallucination. The model tends to fill gaps with invented material rather than admit it doesn’t know.

How Frequently These Errors Occur

The most rigorous study on the topic was published by researchers from RegLab and Stanford’s Human‑Centered AI in the Journal of Empirical Legal Studies in 2025. The data are uncomfortable and worth looking at clearly.

• General‑purpose tools (ChatGPT, Claude, Llama) hallucinate between 58 % and 82 % of legal queries when used without additional context.
• Specialized legal AI tools that use RAG (retrieval‑augmented generation) and anchor the model to real legal databases lower that percentage, but do not eliminate it.
• Lexis+ AI fails on more than 17 % of queries.
• Westlaw AI‑Assisted Research fails on roughly 33 % of queries.

The ACEDS + Secretariat 2025 report shows that 31 % of legal professionals cite hallucinations as the main barrier to AI adoption. This is not paranoid perception; it is an informed response to daily reality.

Why “Legal‑Specialized” Tools Also Fail

A common belief is that a tool built “for lawyers” and backed by a professional database eliminates the problem. Stanford’s data debunk that notion.

RAG is a real improvement: it forces the model to ground its answers in documents retrieved from a legal corpus. Yet the model remains a language model and can misinterpret retrieved material, mix sources, omit nuances, or re‑phrase with a bias that changes a ruling’s meaning.

Some vendors’ marketing has not helped. Not long ago, a major market player claimed its product delivered “100 % citation‑linked legal references free of hallucinations.” After Stanford’s study, the company publicly qualified that language. The operational takeaway for a legal executive is simple: specialized tools are better than general‑purpose ones and deserve a place in the firm’s tech stack, but they do not remove the need for human verification.

Who Is Responsible When AI Gets It Wrong

In Spain, liability always falls on the professional who signs the document.

Organic Law 5/2024 on the Right of Defense and the Spanish Bar’s Code of Ethics are clear. The duties of truthfulness, procedural good faith, and professional diligence are personal obligations of the lawyer. Using a technological tool does not dilute them; it reinforces them.

The Code even defines a concept applicable here: “external pressure.” When an AI system generates a well‑structured analysis, the temptation to sign it without questioning grows. The Code expressly prohibits that. Professional judgment must remain active, not delegated.

Instruction 2/2026 of the General Council of the Judiciary, although aimed at judges, carries the same principle: effective human control, not substitution of professional judgment, and the requirement that AI output does not condition the freedom of assessment.

Sanctions are already arriving. In Spain, fines can reach €6,000 under the Code of Ethics, plus disciplinary penalties and reputational damage. In the United States, individual fines of up to $10,000 and the disqualification of entire teams have been imposed for AI‑generated fabricated citations.

Five Measures Every Law Firm Should Implement

Human supervision is not a patch added at the end; it is part of process design. These are the minimum points any firm using AI for legal work should cover.

1. Internal AI Use Policy

A brief document that defines which tools are authorized, for which tasks, with what data, and under whose responsibility. Without a written policy, each professional improvises. Improvisation is what produces headline‑making judicial errors.

2. Prohibit Direct Use in Filings

AI output should never be copied into a pleading, opinion, or appeal without line‑by‑line verification. Judicial citations, in particular, must be cross‑checked against primary sources (CENDOJ, official bulletins, trusted databases).

3. Conscious Tool Selection

Using a general‑purpose AI is not the same as using a legal‑specific AI with RAG on validated legal bases. The error‑rate difference is significant. When evaluating options, ask the vendor for:

• Documented hallucination rate from independent testing.
• Legal sources the model relies on.
• Traceability mechanisms (can you see where each citation comes from?).
• Update protocols when regulations change.

4. Logging and Traceability

Saving prompts and responses allows reconstruction of what happened if an error surfaces. It is useful for internal process improvement and, if needed, to demonstrate diligence before a professional board or a court.

5. Team Training

Less than 50 % of legal professionals receive adequate AI training, according to Litify. The problem is not technical; it is operational. A team that understands how and why a model fails makes fewer mistakes than one that assumes the tool “is already validated.”

Operational Conclusion

Hallucinations exist and will not disappear soon. Abandoning AI is not the answer either, because the operational advantage of using it correctly is real and measurable.

The difference between firms that adopt this technology successfully and those that end up in headlines is thin. The former understand that AI amplifies a lawyer’s work, not replaces it. Judgment, signature, and responsibility remain human.

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If your firm is planning how to integrate AI into legal processes with the right safeguards (internal policy, tool selection, human supervision built from the start), at Artekia we have been developing these kinds of solutions for a while. If you want to explore what makes sense for your case, we can have a 30‑minute conversation.

Sources

• ACEDS + Secretariat AI in Law Report 2025.
• Litify State of AI Report 2025.
• Magesh, Surani, Dahl et al. “Hallucination‑Free? Assessing the Reliability of Leading AI Legal Research Tools.” Journal of Empirical Legal Studies, 2025.
• Damien Charlotin, “AI Hallucination Cases Database.”
• Spanish Bar, “The Legal Hallucination That Won’t Stop.”
• Economist & Jurist, “Deontology and Professional Ethics of the Lawyer in the Age of AI,” March 2026.
• Código Deontológico de la Abogacía Española.
• Ley Orgánica 5/2024 del Derecho de Defensa.
• Instrucción 2/2026 del CGPJ.