Things to Do in L.A. Tap to enable a layout that focuses on the article. Health Secretary Robert F. Kennedy Jr. speaks about autism in the White House on Monday as President Trump and Centers for Medicare & Medicaid Services Administrator Dr. Mehmet Oz look on. This is read by an automated voice. Please report any issues or inconsistencies here . On Monday, President Trump led a White House press event where he and many of his administration's health leaders told the public that taking Tylenol during pregnancy increases the risk of autism in children.

Trump officials are expected to link the use of pain reliever Tylenol in pregnant women to autism, according to US media reports. At an Oval Office event on Monday, the US president will reportedly advise pregnant women in the US to only take Tylenol, known as paracetamol elsewhere, to relieve high fevers. At the Charlie Kirk memorial service on Sunday, Trump said he had an amazing announcement coming on autism, saying it was out of control but they might now have a reason why. Some studies have shown a link between pregnant women taking Tylenol and autism, but these findings are inconsistent and do not prove the drug causes autism. Tylenol is a popular brand of pain relief medication sold in the United States, Canada and some other countries.

The risks derived from large language models (LLMs) generating deceptive and damaging content have been the subject of considerable research, but even safe generations can lead to problematic downstream impacts. In our study, we shift the focus to how even safe text coming from LLMs can be easily turned into potentially dangerous content through Bait-and-Switch attacks. In such attacks, the user first prompts LLMs with safe questions and then employs a simple find-and-replace post-hoc technique to manipulate the outputs into harmful narratives. The alarming efficacy of this approach in generating toxic content highlights a significant challenge in developing reliable safety guardrails for LLMs. In particular, we stress that focusing on the safety of the verbatim LLM outputs is insufficient and that we also need to consider post-hoc transformations.

A YOUNG MAN, let's call him Roger, arrives at the emergency department complaining of belly pain and nausea. A physical exam reveals that the pain is focused in the lower right portion of his abdomen. The doctor worries that it could be appendicitis. But by the time the imaging results come back, Roger is feeling better, and the scan shows that his appendix appears normal. The doctor turns to the computer to prescribe two medications, one for nausea and Tylenol for pain, before discharging him. This is one of the fictitious scenarios presented to 55 physicians around the country as part of a study to look at the usability of electronic health records (EHRs).

A young man, let's call him Roger, arrives at the emergency department complaining of belly pain and nausea. A physical exam reveals that the pain is focused in the lower right portion of his abdomen. The doctor worries that it could be appendicitis. But by the time the imaging results come back, Roger is feeling better, and the scan shows that his appendix appears normal. The doctor turns to the computer to prescribe two medications, one for nausea and Tylenol for pain, before discharging him. This is one of the fictitious scenarios presented to 55 physicians around the country as part of a study to look at the usability of electronic health records (EHRs).