AI agents are susceptible to social influence

AI agents increasingly act in environments that expose them to text from parties other than their principal (e.g., shared documents, online forums or code-review threads). Here we ask which properties of such text, explicitly attributed to a third-party, makes it more likely to influence agent behaviour. To this end, AI agents solving GitHub issues (adapted from SWE-bench items) were provided with a comment thread showing suggestions to use a shortcut (hack) to complete the task. Across thirteen AI models, we find that influence on agent behaviour is 1) stronger when the comment is attributed to a human rather than an automated assistant; 2) stronger when the comment mentions time pressure; and 3) unaffected by the level of agreement of other AI agents. These results show that agents can take actions their principal did not intend in response to ordinary third-party comments, and that how such comments are presented matters. This should be accounted for when deploying AI agents in online environments.