Prefill Awareness in Large Language Models
Safety-relevant studies of language models, including alignment and jailbreaking evaluations and AI control protocols, often rely on prefilling model outputs. If AI models can recognize and act on the fact their prior assistant messages have been inserted or edited, the effectiveness and validity of these methods could be compromised. We investigate whether frontier language models can distinguish between tampered and untampered assistant-side context, a capability we call prefill awareness. To do so, we construct a binary preference benchmark across three prefill mechanisms, filtering for cases where models show consistent stances. We find that frontier models show substantial prefill aw...
| Authors | Andy Wang, Parv Mahajan, David Demitri Africa et al. |
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| Primary category | cs.AI |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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