Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG

• URL: http://arxiv.org/abs/2602.21447v1
• Date: Tue, 24 Feb 2026 23:52:27 GMT
• Title: Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG
• Abstract summary: Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics.<n>We introduce MMA-RAGT, an inference-time control framework governed by a Modular Trust Agent (MTA) that maintains an approximate belief state.
• Score: 5.4716896485317195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics across retrieval, planning, and generation components. We formulate this security challenge as a Partially Observable Markov Decision Process (POMDP), where adversarial intent is a latent variable inferred from noisy multi-stage observations. We introduce MMA-RAG^T, an inference-time control framework governed by a Modular Trust Agent (MTA) that maintains an approximate belief state via structured LLM reasoning. Operating as a model-agnostic overlay, MMA-RAGT mediates a configurable set of internal checkpoints to enforce stateful defence-in-depth. Extensive evaluation on 43,774 instances demonstrates a 6.50x average reduction factor in Attack Success Rate relative to undefended baselines, with negligible utility cost. Crucially, a factorial ablation validates our theoretical bounds: while statefulness and spatial coverage are individually necessary (26.4 pp and 13.6 pp gains respectively), stateless multi-point intervention can yield zero marginal benefit under homogeneous stateless filtering when checkpoint detections are perfectly correlated.

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