Causal Inference-Guided Adversarial Detection and Mitigation in Medical Large Language Model Agent Architectures
Keywords:
causal inference, adversarial robustness, large language models, medical AI agents, system architecture, healthcare safety, counterfactual reasoningAbstract
The integration of large language models (LLMs) into autonomous agent architectures for clinical decision support, diagnostic reasoning, and patient-facing triage introduces unprecedented capabilities and simultaneous vulnerabilities. Medical LLM agents, characterized by multi-step tool-use, retrieval-augmented generation, and interactive reasoning loops, expand the attack surface for adversarial manipulation beyond single-turn prompt injection to causal perturbation of decision pathways. This paper presents a system-level framework that leverages causal inference to model, detect, and mitigate adversarial threats in medical LLM agent architectures. We argue that purely correlation-based robustness methods are insufficient for safety-critical clinical settings, as they fail to distinguish spurious associations from genuine causal mechanisms exploited by sophisticated adversaries. The proposed approach embeds causal structure learning and counterfactual reasoning within the agent’s execution pipeline, enabling real-time identification of anomalous intervention patterns that deviate from clinically plausible causal graphs. We examine structural trade-offs between detection latency and clinical workflow integration, discuss infrastructure requirements for deploying causal inference modules atop existing LLM stacks, and analyze fairness implications across demographic subgroups when adversarial examples exploit historical disparities encoded in training data. The paper further addresses governance challenges, including liability attribution across agent components, continuous monitoring under distribution shift, and sustainability of computational overhead for causal inference in resource-constrained healthcare settings. Throughout, we maintain a systems perspective, emphasizing how architectural decisions about causal module integration influence robustness, interpretability, and regulatory alignment. We conclude by outlining a research agenda for causally-aware medical agent design that balances real-time performance demands with the epistemic rigor required for high-stakes clinical environments.
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