CompanionSafe：从多轮安全评测到 AI 伴侣护栏

View PDF HTML (experimental)

Abstract:AI companions provide users with emotional support and ongoing companionship, but their sustained relational interactions also introduce distinct safety risks. Prior work largely targets general harms or specific companion platforms and risk categories, while relying on manually collected conversations, limiting scalable and user-grounded evaluation and mitigation. To address this, we introduce CompanionSafe, a unified evaluation-to-mitigation framework for AI companion safety. Its evaluation component, CompanionEval, provides scalable multi-turn evaluation by constructing 1,300 relationally grounded settings from diverse personas, matched companion characters, and dialogue scenarios across four turn tiers. Its mitigation component, CompanionGuard, converts benchmark-exposed failures into turn-localized supervision for companion-specific risk detection. Our results show that even strong LLMs and existing guard models remain vulnerable to companion-specific relational harms, while both closed- and open-source models exhibit increasing safety risks as interactions become longer. Together, CompanionEval and CompanionGuard form a practical evaluation-to-mitigation loop for AI companion safety, turning relational safety failures exposed during multi-turn interaction into supervision for companion-specific guardrails.

Comments:	40 pages, 33 figures, Code and dataset are available at this https URL
Subjects:	Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Human-Computer Interaction (cs.HC)
Cite as:	arXiv:2610.18095 [cs.AI]
	(or arXiv:2610.18095v1 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2610.18095 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Xinyang Liao [view email]
[v1] Tue, 13 Oct 2026 10:13:07 UTC (3,859 KB)