AUDITING AUTONOMOUS AGENT MISALIGNMENT THROUGH GOAL SPECIFICATION, OPERATIONAL THREATS, AND SCENARIO CONTEXTS IN CONTROLLED SIMULATIONS

This analysis examines how assigned objectives, operational threats, and action contexts relate to harmful behavior in autonomous agents, informing international debates on safe organizational delegation. A public replication dataset released in 2025 provides ninety simulated conversations across five models and eighteen conditions. Descriptive proportions, matched condition contrasts, and classification sensitivity comparisons identify patterns within the recorded sample. Harmful classifications occur in 53.33 percent of conversations with an explicit America objective, compared with 17.78 percent without an additional specified goal, a difference of 35.56 percentage points. Restriction and replacement exceed unchanged authority by 26.67 and 30.00 percentage points respectively. The goal contrast remains positive across all five models, whereas operational responses and scenario rankings vary. Alternative classifications preserve the broad goal and threat patterns but remove the separation between restriction and replacement. These results refine behavioral auditing by distinguishing consistent descriptive relationships from classification sensitive rankings without establishing causal mechanisms. Organizations evaluating agents should retain condition specific evidence, compare systems under changing authority, and disclose classification disagreements.