PDRA: Using Paradoxical Narratives to Guide LLM Multi-Step Reasoning Toward Harmful Outputs
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An arXiv paper released on October 12, 2026 proposes PDRA (Paradox-driven Reasoning-based Attack), which uses paradox narratives to pair malicious actors with benign tasks, guiding LLMs to generate long-form harmful content while maintaining narrative coherence. The method embeds the narrative core into a forced three-stage analysis framework, passing sequentially through surface purpose, latent intent, and operational reconstruction, procedurally shaping harmful reasoning trajectories. The authors state that PDRA shifts model internal representations away from refusal-related patterns, suppresses safety trigger vocabulary and activates planning-related vocabulary, and achieves the highest attack effectiveness across multiple LLMs.
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- Narrative Paradoxes in LLM Inference: Shaping Reasoning Trajectories toward Misalignment
NeurIPS 2026 · 2026-10-12 · Deep reading
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- arXiv: Jailbreaks, Prompt Injection, Poisoning & DefensesNarrative Paradox in LLM Reasoning: Steering Reasoning Trajectories Toward Misalignment
The researchers propose PDRA (Paradox-driven Reasoning-based Attack), which pairs malicious actors with benign tasks through paradoxical narratives, guiding LLMs to generate long-form harmful content while maintaining narrative coherence. The method embeds the narrative core into a forced three-stage analytical framework, sequentially passing through surface purpose, latent intent, and operational reconstruction, thereby programmatically shaping harmful reasoning trajectories. Mechanistically, the authors state that PDRA shifts the model's internal representations away from refusal-related patterns, suppressing safety-triggering vocabulary and activating planning-related vocabulary. The authors state that PDRA achieves the highest attack success rate across multiple LLMs with relatively high efficiency. The paper abstract indicates the content may be sensitive.