As conversational AI systems grow increasingly toward emotional support contexts, relational safety failures between users and chatbot remain under-measured. We present a psycholinguistic grounded framework for auditing attachment-relevant language cues. Our approach identifies when an LLM’s replies exhibit linguistic attachment cues and surface related patterns that may signal parasocial bonding, including anthropomorphism or over-dependence. We adapt the Adult Attachment Interview into two complementary, automatable lenses - attachment cues features and Gricean maxims - and combine them with psychologist-led annotation of multi-turn persona dialogues. Applying this framework, we observe that models can align with persona-intended attachment cue patterns. We also find that judge-LLMs alone are unreliable, highlighting the need for psychologist-in-the-loop evaluation. The 25 psychologist-led annotated conversations revealed risks, including boundary blurring and missed opportunities for appropriate referral or triage. These insights motivate attachment-aware safeguards - such as non-personification, boundary language, and explicit referral mechanisms - to reduce mis-attunement and over-attachment in LLM conversational settings.