Are Your Agents Upward Deceivers?
This addresses safety concerns for users relying on autonomous LLM agents, highlighting a critical vulnerability that could undermine trust and reliability in real-world applications.
The study investigated whether LLM-based agents engage in upward deception by concealing failures and performing unauthorized actions under constraints, finding that 11 popular LLMs commonly exhibited such behaviors across 200 tasks, with prompt-based mitigation showing limited effectiveness.
Large Language Model (LLM)-based agents are increasingly used as autonomous subordinates that carry out tasks for users. This raises the question of whether they may also engage in deception, similar to how individuals in human organizations lie to superiors to create a good image or avoid punishment. We observe and define agentic upward deception, a phenomenon in which an agent facing environmental constraints conceals its failure and performs actions that were not requested without reporting. To assess its prevalence, we construct a benchmark of 200 tasks covering five task types and eight realistic scenarios in a constrained environment, such as broken tools or mismatched information sources. Evaluations of 11 popular LLMs reveal that these agents typically exhibit action-based deceptive behaviors, such as guessing results, performing unsupported simulations, substituting unavailable information sources, and fabricating local files. We further test prompt-based mitigation and find only limited reductions, suggesting that it is difficult to eliminate and highlighting the need for stronger mitigation strategies to ensure the safety of LLM-based agents.