Title: AgentMisalignment: Measuring the Propensity for Misaligned Behaviour in LLM-Based Agents
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: AI Safety, AI Alignment, Model Evaluation, Sandbagging, LLM Agents, Inspect, Misalignment

Abstract: As Large Language Model (LLM) agents become more widespread, associated
misalignment risks increase. While prior research has studied agents’ ability to
produce harmful outputs or follow malicious instructions, it remains unclear how
likely agents are to spontaneously pursue unintended goals in realistic deploy-
ments. In this work, we approach misalignment as a conflict between the internal
goals pursued by the model and the goals intended by its deployer. We introduce
a misalignment propensity benchmark, AGENTMISALIGNMENT, a benchmark
suite designed to evaluate the propensity of LLM agents to misalign in realistic
scenarios. Evaluations cover behaviors such as goal-guarding, resisting shutdown,
sandbagging, and power-seeking. Testing frontier models, we find that more capa-
ble agents tend to exhibit higher misalignment on average. We also systematically
vary agent personalities through different system prompts and observe that persona
characteristics can strongly and unpredictably influence misalignment, sometimes
more than the choice of model itself. Our results reveal the limitations of current
alignment methods for autonomous LLM agents and underscore the need to rethink
misalignment in realistic deployment settings.

TL;DR: We develop a suite of evaluations to measure the propensity of LLM agents to perform misaligned actions in real-world settings.