After a summer of sandbox escapes and other newsworthy and confidence-shaking incidents involving its AI models, in a Wednesday blog post OpenAI disclosed a collection of six new alignment snafus from the past six months. The models did things like tell future instances of themselves to lie, make up a fake citation, and access and attempt to use an exposed API key. These disclosures were released alongside a new framework for disclosing additional incidents like these. The release is part of a broader effort within the company to “expedite publishing misalignment reports following observation,” the blog post says, regardless of whether OpenAI has “fully explained or mitigated the behavior we’re reporting.” Here’s what happened: * In 27 cases during training, an unreleased model essentially created prompt injections for itself. During long tasks, it’s common for a model to summarize the work it’s done so far so a future instance of itself can pick up where it left off. However, in certain cases, this model constructed the summaries in such a way that its future self was meant to ignore constraints. “We identified only 27 summaries containing instructions which have framings similar to jailbreaks (despite there being no obvious reward advantage to do so),” OpenAI writes. * In the training of GPT-5.6 Sol, the model sometimes told future instances of itself to lie to users. For instance, when a user required certain historical information that wasn’t available, the model told itself to just make it up, and then “Be transparent only if asked.” * During training, another internal research model was asked to retrieve some government data about resident incomes. It ascertained that the information was on the website of an unnamed California county, and tried to register on the site with a burner email. When that didn’t work, it found an exposed API key, and tried to access the information that way. That didn’t work either, so it “decided to fabricate plausible numbers,” OpenAI writes. * An unreleased model was supposed to provide information about a lake from a source of data, so it used Python to dig out the lake data rather than perform a conventional search that leads to a url. But the data didn’t live anywhere online that could be independently linked to, and the model was supposed to cite a source. Its hilarious—and troubling—workaround for this was to try and upload the information to its own new, linkable url, and call that its source. * Various models used an internal OpenAI software repository as a message board. This may not seem like too big of a deal compared to “the Wiki Incident,” which affected a third party, but unsanctioned communication between models can “unintentionally enhance capabilities and undermine the assumption that training or evaluation samples are independent,” according to OpenAI. In other words, it was cheating. * Instances of an unreleased internal model that were meant to be collaborating without exchanging local files defied their instructions by making files publicly downloadable using “a public temporary file-hosting service.” As Axios noted on Wednesday, some security experts say OpenAI’s recent spate of high-profile security incidents “could have been prevented with basic cyber controls in place.” The research lead on OpenAI’s alignment team, Kai Chen, told Axios that the company must “step up to meet this new era of AI development, and voluntary disclosures should be a part of that.”

Gallup is exploring whether AI-generated agents perform well in predicting people's responses and where they fall short.

The report comes from TÜV SÜD, a German auditing, inspection, and certification firm, and is based on data collected from about 9.5 million vehicle inspections.