Adaptive Stress Testing Black-Box LLM Planners

• URL: http://arxiv.org/abs/2505.05665v2
• Date: Fri, 10 Oct 2025 19:42:23 GMT
• Title: Adaptive Stress Testing Black-Box LLM Planners
• Authors: Neeloy Chakraborty, John Pohovey, Melkior Ornik, Katherine Driggs-Campbell,
• Abstract summary: Large language models (LLMs) have recently demonstrated success in generalizing across decision-making tasks.<n>But their tendency to hallucinate unsafe and undesired outputs poses risks.<n>We argue that detecting such failures is necessary, especially in safety-critical scenarios.
• Score: 6.506759042895813
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) have recently demonstrated success in generalizing across decision-making tasks including planning, control, and prediction, but their tendency to hallucinate unsafe and undesired outputs poses risks. We argue that detecting such failures is necessary, especially in safety-critical scenarios. Existing methods for black-box models often detect hallucinations by identifying inconsistencies across multiple samples. Many of these approaches typically introduce prompt perturbations like randomizing detail order or generating adversarial inputs, with the intuition that a confident model should produce stable outputs. We first perform a manual case study showing that other forms of perturbations (e.g., adding noise, removing sensor details) cause LLMs to hallucinate in a multi-agent driving environment. We then propose a novel method for efficiently searching the space of prompt perturbations using adaptive stress testing (AST) with Monte-Carlo tree search (MCTS). Our AST formulation enables discovery of scenarios and prompts that cause language models to act with high uncertainty or even crash. By generating MCTS prompt perturbation trees across diverse scenarios, we show through extensive experiments that offline analyses can be used at runtime to automatically generate prompts that influence model uncertainty, and to inform real-time trust assessments of an LLM. We further characterize LLMs deployed as planners in a single-agent lunar lander environment and in a multi-agent robot crowd navigation simulation. Overall, ours is one of the first hallucination intervention algorithms to pave a path towards rigorous characterization of black-box LLM planners.

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