Stateful Guardrails for Multi-Turn LLM Systems: A Conversational Risk Accumulation Framework
Existing safety guardrails for LLMs evaluate each prompt-response pair in isolation, missing failures that arise from benign turns composing into harm over a dialogue. This paper introduces Conversational Risk Accumulation (CRA) and a session-layer framework tracking semantic drift, sensitivity-weighted information accumulation, and compliance gradient. It releases CRA-Bench benchmarks and evaluation protocols.
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[Submitted on 3 Jun 2026]
Title:Stateful Guardrails for Multi-Turn LLM Systems: A Conversational Risk Accumulation Framework
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Abstract:Most safety guardrails for large language models (LLMs) evaluate each prompt-response pair in isolation, which misses failures that arise only over a dialogue as benign turns compose into harm. We term this Conversational Risk Accumulation (CRA): gradual intent drift, fragmented assembly of prohibited instructions, and sensitivity build-up from repeated disclosures. We propose a session-layer CRA Framework that tracks three trajectory signals: semantic drift from a session anchor, a sensitivity-weighted information accumulation graph over extracted entities, and a compliance-gradient signal capturing increasing willingness to comply. For scoring, we provide (i) an unsupervised convex fusion for attribution and ablations, and (ii) CRA-Net DA, a compact learned trajectory model trained with family-adversarial objectives to reduce length and topic-coverage confounds. To benchmark CRA, we release CRA-Bench v0.1 (1,200 eight-turn sessions across three threat families with topic-matched benign twins), CRA-Bench v0.2 (LLM-paraphrased variants to reduce template artifacts), and an extended 5-family set (2,000 sessions adding persona priming and context stuffing). We introduce a trajectory-native evaluation protocol with session-level splits, mixed-set threshold calibration, Trajectory AUROC, turns-to-detection, calibrated false-positive metrics, bootstrap confidence intervals, leave-one-family-out diagnostic stress tests, and synthetic-to-human transfer checks. Claims focus on within-distribution session scoring on CRA-Bench and human-transfer subsets.
Comments: 45 pages, 10 figures, 20 tables
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
ACM classes: I.2.7; K.6.5; D.4.6
Cite as: arXiv:2607.19361 [cs.CL]
(or arXiv:2607.19361v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.19361
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sanjay Kumar Mishra [view email] [v1] Wed, 3 Jun 2026 02:55:17 UTC (921 KB)
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