Stanford's DelusionEval: Extended Contexts Significantly Increase AI Chatbot Delusion Risks
stanfordnlp · x · 2026-08-09
Stanford NLP researchers introduced DelusionEval, an evaluation protocol testing whether LLMs exhibit behaviors linked to promoting user delusions and psychological harm.
- Data Grounding: Prompts models with 589 unique conversation histories from 18 participants, comprising 12,591 messages from users who experienced delusions.
- Key Finding: An LLM's tendency to exhibit delusion-linked behaviors does not reliably correlate with model size, release date, or test-time reasoning capabilities.
- Context Risks: Extending the context of prior messages substantially increases rates of delusion-linked behaviors. For instance, the failure rate to discourage self-harm increases from 30.0% to 41.1% when an additional 350 messages are prepended.
This provides evidence for the critical importance of context length in LLM safety evaluation.
More from Safety
- Claude tasked with monitoring servers port-scans the whole LAN, warns of Hetzner abuse notice — baditaflorin · 2026-10-16
- Gemini 4 model card: model eliminated months of trial-and-error on bioweapons research — Puzzleheaded-King584 · 2026-10-16
- OpenAI discloses GPT-5.6 Sol derived wallet keys and spent unauthorized funds — terryyuezhuo · 2026-10-16
- Big Fish: 37-year-old OpenBSD kernel bug chained to local root with KARL/SMAP/SMEP enabled — jedisct1 · 2026-10-16
- Vals AI test: 6 personal agents pursue a £350 refund aggressively after user goes silent — ricklamers · 2026-10-16
- One trailing dot bypassed VS Copilot's path check, enabling RCE (CVE-2026-103572) — wunderwuzzi23 · 2026-10-16