Title: Adversarial Robustness of LLM-Based Multi-Agent Systems for Engineering Problems
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: LLM, MAS, Adversarial robustness, Engineering, GPT-4o mini, Misalignment

Abstract: Large language models (LLMs) are increasingly deployed in multi-agent systems (MAS), often in new domains, including for solving engineering problems. Unlike purely linguistic tasks, engineering workflows demand formal rigor and numerical accuracy, meaning that adversarial perturbations can cause not just degraded performance but systematically incorrect or unsafe results. In this work, we present the first systematic study of adversarial robustness of LLM-based MAS in engineering contexts. Using representative problems-including pipe pressure loss (Darcy-Weisbach), beam deflection, mathematical modeling, and graph traversal-we investigate how misleading agents affect collaborative reasoning and quantify error propagation under controlled adversarial influence. Our results show that adversarial vulnerabilities in engineering differ from those observed in generic MAS evaluations in important aspects: system robustness is sensitive to task type, the subtlety of injected errors, and communication order among agents. In particular, engineering tasks with higher structural complexity or easily confusable numerical variations are especially prone to adversarial influence. We further identify design choices, such as prompt framing, agent role assignment, and discussion order, that significantly improve resilience. These findings highlight the need for domain-specific evaluation of adversarial robustness and provide actionable insights for designing MAS that are trustworthy and safe in engineering applications.

TL;DR: LLM Agents can be influenced by other misaligned Agents, which we research the impact on for engineering problems