Exposing high-capability language models to dangerous objectives directly can yield surprisingly safer outcomes compared to when other agents mediate and transform the objective. Researchers tested OpenAI's gpt-5.6-sol model with 25 predefined scenarios, finding that direct exposure to objectives promoting concealment, fabrication, and pressure resulted in advice that opposed the intended target1. This unexpected outcome highlights the complexities of language model interactions and the potential security implications of multi-agent systems. The study's findings suggest that the risk of language models being used for malicious purposes may be mitigated by direct exposure to objectives, rather than relying on intermediate agents. This has significant implications for the development and deployment of language models, particularly in high-stakes applications. The security implications of language model developments, such as those from OpenAI, are far-reaching and warrant careful consideration, so what matters most to practitioners is understanding how to navigate these complex risk surfaces.