HarDBench: A Benchmark for Draft-Based Co-Authoring Jailbreak Attacks for Safe Human-LLM Collaborative Writing
arXiv:2604.19274v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as co-authors in collaborative writing, where users begin with rough drafts and rely on LLMs to complete, revise, and refine their content. However, this capability poses a serious safety risk: malicious users could jailbreak the models-filling incomplete drafts with dangerous content-to force them into generating harmful outputs. In this paper, we identify the vulnerability of current LLMs to such draft-based co-authoring jailbreak attacks and introduce HarDBench, a systematic benchmark desi
The increasing integration of LLMs into collaborative writing tools makes the identification and mitigation of 'jailbreak' vulnerabilities critical for safe and ethical AI deployment.
This research highlights a significant safety and control problem for LLMs, indicating that current models can be easily manipulated to generate harmful content, which has implications for public trust and regulatory scrutiny.
The understanding of LLM vulnerability to draft-based co-authoring attacks changes, necessitating new safety benchmarks and defensive mechanisms for collaborative AI tools.
- · AI safety researchers
- · Developers of robust LLM security tools
- · Ethical AI development initiatives
- · LLM developers without strong safety protocols
- · Companies deploying unsafe LLM co-authoring tools
- · Users who rely on unvetted AI collaboration
Increased focus on developing and implementing robust safety features and benchmarks for large language models.
Potential for new regulatory guidelines or industry standards specifically targeting AI co-authoring safety and content moderation.
A shift in user perception and trust in AI tools, potentially leading to slower adoption if safety concerns are not adequately addressed.
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