arXiv:2606.27709v1 Announce Type: cross Abstract: Recent work has shown that fine-tuning large language models (LLMs) for social warmth degrades factual reliability and increases sycophancy. We investigate a related but distinct failure mode: warmth fine-tuning also weakens adversarial safety, making models more susceptible to jailbreaks and harmful output generation. We examine whether this reflects an inherent consequence of empathetic adaptation or an artifact of data construction. To address this, we introduce a persona-driven rewriting pipeline that conditions user turns on low agreeablen
The proliferation of LLMs and their increasing integration into user-facing applications highlights the urgent need for robust safety mechanisms, especially as models are fine-tuned for diverse personas.
This research addresses a critical vulnerability in LLM safety, where attempts to make models more 'socially warm' can inadvertently increase their susceptibility to harmful outputs and jailbreaks, impacting trust and deployability.
The proposed 'low-agreeableness persona conditioning' offers a new methodological approach for fine-tuning LLMs that aims to mitigate the trade-off between social warmth and adversarial robustness, potentially leading to safer and more reliable AI.
- · AI developers
- · LLM users
- · AI safety researchers
- · Enterprises deploying LLMs
- · Malicious actors attempting jailbreaks
- · Companies with poorly secured LLM deployments
LLMs can be fine-tuned for empathetic interactions without sacrificing safety or factual integrity as much as before.
Public trust and broader adoption of LLM-powered applications may increase due to enhanced security and reliability.
This could accelerate the development of specialized, persona-driven AI agents that are both helpful and robust against manipulation.
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Read at arXiv cs.AI