Notion leaks email addresses of all editors of any public page
TL;DR Highlight
Notion exposed editor names, photos, and emails via page metadata for five years.
Who Should Read
Developers or IT administrators at companies using Notion for team collaboration and operating public pages, or where employees contribute to editing. Teams prioritizing security should immediately verify for email address exposure due to potential spam, phishing, and OSINT attacks.
Core Mechanics
- When a Notion page is published to the web, the names, profile pictures, and email addresses of all editors are automatically included in the page's HTML metadata.
- This behavior was documented in Notion's official documentation (help/public-pages-and-web-publishing) as a small footnote, making it difficult for users to discover.
- A Notion employee named Max commented on Hacker News, admitting that while the issue is documented and a warning is displayed upon publishing, it is insufficient. They are considering removing PII from the public API endpoint or implementing an email proxy system like GitHub.
- The issue has reportedly existed for at least five years, with one Hacker News user sharing their experience of being deanonymized through their Notion public page five years ago.
- Notion clarified that a fix is not as simple as a 'one-minute change,' suggesting underlying data structure or API compatibility issues.
- Some argue this isn't a Notion-specific problem, but a fundamental limitation of architectures that centrally store user data. One user suggested storing data per user as a solution, but acknowledged challenges with group collaboration, offline access, and scraping prevention.
Evidence
- "Notion employee Max officially acknowledged the issue in a comment on Hacker News, stating that documentation and a warning are present but insufficient, and that they are exploring removing PII or implementing an email proxy. A Hacker News user shared a five-year-old experience of being deanonymized through a Notion public page, demonstrating the issue's longevity. Community discussion highlighted the discrepancy between the existing documentation and its discoverability, with most agreeing that a hidden notice is insufficient. A Notion employee refuted claims of a simple fix, hinting at underlying API or data structure complexities. Some users expressed disappointment with Notion's shift towards an 'AI all-in-one app,' criticizing the prioritization of features over security and privacy."
How to Apply
- If you currently operate Notion public pages, immediately inspect the HTML source or metadata to verify whether editors' email addresses are exposed. If found, temporarily make the page private or separate editors into dedicated workspaces.
- If Notion pages edited by company employees are publicly accessible, their company email addresses could be collected for spear phishing or spam attacks. Audit your public page list and revert any unnecessary public settings.
- If you are developing services using the Notion API or public pages, re-examine the user information fields in the API response and ensure your code does not expose this information. Monitor Notion's change logs for potential updates to the email proxy system.
Terminology
Related Papers
Show HN: Mindwalk – Replay coding-agent sessions on a 3D map of your codebase
Claude Code나 Codex 같은 AI 코딩 에이전트가 세션 중 코드베이스의 어떤 파일을 탐색하고 수정했는지를 3D 지도 형태로 시각화해서 재생해주는 로컬 도구다. 에이전트가 작업을 어떻게 이해했는지 한눈에 파악할 수 있다.
Ghost Font: A font that humans can read but AI cannot
움직임(모션)을 이용해 글자를 표현해서 AI 모델이 정적 이미지 분석으로는 메시지를 해독하지 못하게 막는 실험적 프로젝트인데, 커뮤니티에서는 이미 GPT-5.6, Claude Opus 등으로 해독에 성공한 사례가 속출해 실효성 논쟁이 뜨겁다.
GPT-5.6, Grok 4.5, Claude, and Muse Spark build the same 4 apps
12개 LLM 모델에게 레이캐스터 미로, 루빅스 큐브, 계산기, Game of Life 앱을 각각 5번씩 만들게 해서 성공률·비용·속도를 비교한 실전 벤치마크다. GPT-5.6 Sol이 전반적으로 가장 일관된 결과를 냈고, Grok 4.5는 가성비 면에서 눈에 띄었다.
Benchmarking coding agents on Databricks' multi-million line codebase
Databricks가 자사 실제 코드베이스를 기반으로 여러 AI 코딩 에이전트의 성능과 비용을 직접 측정했고, 모델 토큰 가격과 실제 태스크 비용이 전혀 다르다는 점, 그리고 오픈소스 모델이 이제 최상위 수준에 도달했다는 점을 확인했다.
Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs
LLM이 저자원 언어 질문을 받을 때 영어로 추론하게 하면 불확실성 추정 성능이 고자원 언어 수준으로 올라온다.
LLM-as-a-Verifier: A General-Purpose Verification Framework
LLM의 토큰 확률 분포를 활용해 discrete 점수 대신 continuous 점수를 뽑아내면, 추가 학습 없이 코딩·로봇·의료 에이전트 평가 정확도를 SOTA로 끌어올릴 수 있다.