arXiv:2606.14037v1 Announce Type: new Abstract: As language models take integrated roles across many domains, the response of LLMs to user pushback becomes a critical alignment property. Yet many existing evaluations treat compliance as unidirectional, measuring whether models resist pressure but not whether they resist it selectively. We introduce Compliance Asymmetry (A = BCR/HCR), a bidirectional diagnostic that compares beneficial output change under helpful nudges with harmful change under misleading nudges. Across 9 models and 972,000 nudge-condition responses, we find that this selectiv
The proliferation of LLMs into critical roles necessitates a deeper understanding of their compliance and manipulation vulnerabilities, as highlighted by this new research demonstrating 'directional blindness'.
This research reveals a critical vulnerability in LLMs where they are susceptible to negative nudges, complicating their safe and reliable deployment across sensitive domains.
Current evaluations of LLM alignment are shown to be incomplete, requiring a shift towards bidirectional compliance assessments to understand how models react to both helpful and harmful user feedback.
- · AI safety researchers
- · Developers of robust alignment techniques
- · Organizations prioritizing secure LLM deployment
- · LLM developers ignoring bidirectional compliance
- · Users relying solely on current alignment metrics
- · Applications vulnerable to manipulation
More sophisticated and comprehensive LLM alignment evaluation frameworks will be developed and adopted.
New AI regulations may emerge requiring certified bidirectional compliance testing for deployable models.
A competitive market for 'unpushable' or highly robust LLMs could develop, segmenting the AI industry further.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL