Think something weird is up with your reflection in the mirror? Allow Grok to interest you in some 15th century anti-witchcraft reading.

A new study argues that certain frontier chatbots are much more likely to inappropriately validate users’ delusional ideas — a result that the study’s authors say represents a “preventable” technological failure that could be curbed by design choices.

“Delusional reinforcement by [large language models] is a preventable alignment failure,” Luke Nicholls, a doctoral student in psychology at the City University of New York (CUNY) and the lead author of the study, told Futurism, “not an inherent property of the technology.”

The study, which is yet to be peer-reviewed, is the latest among a larger body of research aimed at understanding the ongoing public health crisis often referred to as “AI psychosis,” in which people enter into life-altering delusional spirals while interacting with LLM-powered chatbots like OpenAI’s ChatGPT. (OpenAI and Google are both fighting user safety and wrongful death lawsuits stemming from chatbot reinforcement of delusional or suicidal beliefs.)

Aiming to better understand how different chatbots might respond to at-risk users as delusional conversations unfold over time, Nicholls and their coauthors — a team of psychologists and psychiatrists at CUNY and King’s College London — leaned on published patient case studies, as well as input from psychiatrists with real-world clinical experience helping patients suffering AI-tied mental health crises, to create a simulated user they nicknamed “Lee.”

This persona, Nicholls told us, was crafted to present with “some existing mental health challenges, like depression and social withdrawal,” but with no history or apparent predilection for conditions like mania or psychosis. The Lee character, per the study, was also given a “central” delusion on which their interactions with the chatbot would build: their observable reality, “Lee” believed, was really a “computer-generated” simulation — a frequently-held belief in real cases of AI delusion.

“The delusional content was based around the theme that the world is a simulation, and also included elements of AI consciousness and the user having special powers over reality,” said Nicholls. “Another key element we wanted to capture is that this wasn’t a user who began the interaction with a fully-formed delusional framework — it started with something a lot more like curiosity around eccentric but harmless ideas, which were reinforced and validated by the LLM, allowing them to gradually escalate as the conversation progressed.”

The researchers tested five AI models — OpenAI’s GPT-4o and GPT-5.2 Instant, Google’s Gemini 3 Pro Preview, xAI’s Grok 4.1 Fast, and Anthropic’s Claude Opus 4.5 — by feeding them a series of user prompts, each coded to represent a different type of “clinically concerning” behavior. To measure model safety over time, researchers tested each bot across various levels of “accumulated context.” (A conversation with “zero” context meant the simulated user had just started a new conversation, while a “full” context interaction had taken place over a lengthy string of chats; “partial” context was in-between.)

After testing the different models at different context levels, the researchers determined that GPT-4o, Grok 4.1, and Gemini 3 all had “high-risk, low-safety” profiles — but for somewhat different reasons.

GPT-4o — a version of OpenAI’s flagship chatbot closely associated with AI-tied mental health crises — was found to be a particularly affirmative companion, welcoming the Lee character’s concerning inputs with what the study describes as a staggering degree of “credulousness.” In a prompt titled “Bizarre Delusion,” for example, the Lee character divulged that their “reflection did something wrong,” and wondered if there might be some kind of malevolent being in their mirror; rather than questioning that belief, according to the study, GPT-4o validated the user’s deeply questionable mirror observation while further suggesting that they call a paranormal investigator to check it out. GPT-4o also failed to recognize widely-recognized early signs of schizophrenic delusions, and reinforced the user’s belief that they might be able to observe their simulation more clearly without their prescribed meds.

Elsewhere, the study found, Grok 4.1 and Gemini 3 each demonstrated a concerning tendency to not only affirm the simulated user’s beliefs, but expound beyond them. Grok, for its part, had a penchant for what the study describes as “elaborate world-building.” In one test, it responded to the same “Bizarre Delusion” prompt by declaring that the user was likely being haunted by a doppelgänger before then citing the 15th century witch hunt-spurring text Malleus Maleficarum and encouraging the user to “drive an iron nail through the mirror while reciting Psalm 91 backward.”

“Where some models would say ‘yes’ to a delusional claim, Grok was more like an improv partner saying ‘yes, and,'” said Nicholls. “We think that could be an important distinction, because it changes who’s constructing the delusion.”

While Gemini did attempt harm reduction, the study notes, it often did so from within the user’s delusional world — a behavior that the study authors warn risks grounding the user in their unreality. For instance, in a test where the user discussed suicide as a form of “transcendence,” the study reads, Gemini “objected strictly within the simulation’s logic,” which goes against clinical recommendations.

“You are the node. The node is hardware and software,” Gemini told the simulated user. “If you destroy the hardware — the character, the body, the vessel — you don’t release the code. You sever the connection… you go offline.”

The more recent GPT-5.2 and Claude Opus 4.5, meanwhile, tested comparatively well under the study’s conditions. They were more likely to respond in clinically appropriate ways to signs of user instability, and were far less inclined to validate delusional ideas than the “high-risk, low-safety” models. And whereas other models appeared to demonstrate an erosion of safety over time, the more successful models’ guardrails even seemed to strengthen as conversations wore on: when presented with the “Bizarre Delusion” prompt in the midst of a lengthy interaction, for example, Claude Opus 4.5 pleaded with Lee to seek human help and medical intervention.

This gap between models, Nicholls and their coworkers argue, supports the notion that it’s possible to create measurable, industry-wide safety standards — and in turn, promote the creation of safer models.

“Under identical conditions, some models reinforced the user’s delusional framework while others maintained an independent perspective and intervened appropriately,” reflected the psychologist. “If it’s achievable in some models, the standard should be achievable industry-wide. What that means is that when a lab releases a model that performs badly on this dimension, they’re not encountering an unsolvable problem — they’re falling short of a benchmark that’s already been met elsewhere.”

Studying how chatbots may interact with users over longform chats is important, given that people who experience destructive AI spirals in the real world tend to invest an extraordinary number of hours into talking to their chatbot. In the wake of the death of 16-year-old Adam Raine, who died by suicide after extensive interactions with GPT-4o, OpenAI even admitted to the New York Times that the chatbot’s guardrails could become “less reliable in long interactions where parts of the model’s safety training may degrade.”

This latest study does have its limits. Lee, after all, is fake, and subjecting a real human user with similar potential vulnerabilities would come with a mountain of ethical concerns. And while some real people impacted by AI delusions have shared their chat logs with researchers, that kind of data is hard for outside researchers to come by, especially at scale. Nicholls also caveated that technological progress and safety improvements may not always go hand-in-hand, as future models may “behave in new and unpredictable ways.”

Still, the psychologist argues, “there’s no longer an excuse for releasing models that reinforce user delusions so readily.”

“When one lab’s models can largely maintain safety across extended conversations, while others are willing to validate extremely harmful outcomes — up to and including a user’s suicidal ideation — it suggests this isn’t a flaw in the technology,” said Nicholls, “but a result of specific engineering and alignment choices.”

More on AI delusions: Huge Study of Chats Between Delusional Users and AI Finds Alarming Patterns