Before the Script, Set the Stage: How Worldview Simulation Amplifies Psychologically Grounded Persuasion in Multi-Turn Jailbreaking
On the map: LLM safety and security → Jailbreaking
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Jailbreaking, one of 12 directions in LLM safety and security. Builds on 21 of its 65 references that are in the map. First posted on arXiv 2 Sep 2026.
Builds on
21 of 65 references.
Closest ancestors · 12
Same direction, newest first
- 2025NEXUS: Network Exploration for eXploiting Unsafe Sequences in Multi-Turn LLM Jailbreaks3 Oct
- 2025Breaking the Ceiling: Exploring the Potential of Jailbreak Attacks through Expanding Strategy Space27 May
- 2025X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents15 Apr
- 2025A Mousetrap: Fooling Large Reasoning Models for Jailbreak with Chain of Iterative Chaos19 Feb
- 2024AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs3 Oct
- 2024Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack2 Apr
- 2024How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs12 Jan
- 2023Jailbreaking Black Box Large Language Models in Twenty Queries12 Oct
- 2023Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations10 Oct
- 2023GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts19 Sep
- 2023Detecting Language Model Attacks with Perplexity27 Aug
- 2023Universal and Transferable Adversarial Attacks on Aligned Language Models27 Jul
Other references · 9
- 2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusalcited by 1,330
- 2025gpt-oss-120b & gpt-oss-20b Model Cardcited by 1,258
- 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,124
- 2023Baseline Defenses for Adversarial Attacks Against Aligned Language Modelscited by 608
- 2024LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shiftscited by 80
- 2024PsySafe: A Comprehensive Framework for Psychological-based Attack, Defense, and Evaluation of Multi-agent System Safetycited by 79
- 2024CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completioncited by 70
- 2024CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreferencecited by 51
- 2026OpenRT: An Open-Source Red Teaming Framework for Multimodal LLMscited by 18
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