BluePRINT is introduced, a safety-evaluation framework separating a factorized social-influence strategy space from WORLDVIEWSIM, a cross-turn situational context module, and Monte Carlo Tree Search optimizes turn-level combinations of 18 theory-grounded influence factors across a four-turn trajectory.
Abstract
Multi-turn jailbreak attacks demonstrate that harmful intent can be distributed across dialogue, yet existing methods obscure what conversational mechanisms drive vulnerability. We introduce BLUEPRINT, a safety-evaluation framework separating a factorized social-influence strategy space from WORLDVIEWSIM, a cross-turn situational context module. Monte Carlo Tree Search optimizes turn-level combinations of 18 theory-grounded influence factors across a four-turn trajectory. Across six frontier models, BLUEPRINT achieves near-ceiling ASR on major open-weight and proprietary models, while requiring the fewest average queries (2.46). The resulting trajectories further reveal model-specific vulnerability among resistant targets: each responds to distinct influence factors and strategy transitions, yet all share a common recovery pathway-shifting toward concrete, executable task framing consistently escapes hard-refusal states. Ablations confirm operational cues matter most: making requests actionable has the largest impact, gain framing is unusually potent, and some legitimacy appeals can backfire. These findings suggest robust multi-turn safety requires monitoring not only harmful content, but also how dialogue state makes unsafe requests appear concrete and locally executable.
People increasingly turn to large language models (LLMs) for everyday advice, making ethically charged interpersonal problems a practical moral-advisory context. Most prior work has studied this context through single-turn judgments or pressure-laden rebuttals, assumptions that poorly match how guidance is sought in re...
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PsychJail is presented, a psychology-guided framework for red teaming aligned LLMs through theory-grounded, multi-turn persuasion and establishes psychological jailbreaks as a distinct red-teaming frontier for increasingly interactive LLMs.
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Organizations have long treated clarity, timeliness and candour as the pillars of effective feedback. Yet even well-structured feedback routinely fails to produce learning. This article argues that the dominant delivery-focused framework rests on an incomplete assumption. We introduce psychological interpretability: th...
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A multi-agent refinement process that iteratively improves LLM-driven counter-narratives for persuasiveness, emotional engagement, and shareability is introduced, highlighting a pathway toward scalable, narrative-specific interventions against hate speech and misinformation.
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This work argues that Cooperative AI evaluations should separate what models can do under benign instructions from what they tend to do under realistic civic pressure, and introduces DiffCoop-Civic, a 10-scenario pilot evaluation suite spanning preference understanding, evidence and persuasion, commitment design, asymm...