HarDBench: A Benchmark for Draft-Based Co-Authoring Jailbreak Attacks for Safe Human-LLM Collaborative Writing
Researchers introduce HarDBench, a benchmark exposing how LLMs can be jailbroken through draft-based co-authoring attacks where malicious users seed incomplete documents with harmful content to force unsafe completions. The work systematically evaluates model robustness across high-risk domains including explosives, drugs, weapons, and cyberattacks.
Modelwire context
ExplainerThe key distinction HarDBench draws is that co-authoring attacks exploit the model's role as a completer rather than a responder: the harmful payload is seeded in the user's draft, not in an explicit instruction, which means standard prompt-level safety filters may not catch it at all.
Modelwire has been tracking a wave of domain-specific benchmarks this month, including IndiaFinBench and QuantCode-Bench, but those measure capability. HarDBench sits in a different category: it measures where safety breaks down under realistic usage conditions. The WIRED piece from April 17 on AI-assisted drafting in newsrooms is the more relevant adjacent story here, because it describes exactly the workflow HarDBench stress-tests: a human seeds a document and an LLM completes it. That piece framed the concern as editorial quality and labor; this paper surfaces a harder problem, which is that the same workflow can be weaponized. Together they suggest that co-authoring tools are being deployed before the safety research has caught up.
Watch whether major co-authoring tool vendors (Microsoft Copilot, Google Docs AI, Notion AI) acknowledge HarDBench or publish any red-teaming results against draft-completion attack vectors within the next six months. Silence from that group would confirm the gap the paper implies.
Coverage we drew on
- AI Drafting My Stories? Over My Dead Body · WIRED — AI
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