# Findings: more References

Canonical: https://machines-on-machines.anth.us/reference-lists/findings/untagged/c07340da05d/

- [Open and Emergent Problems in Agentic Privacy and Security: A Contextual Angle](https://research.google/blog/open-and-emergent-problems-in-agentic-privacy-and-security-a-contextual-angle/)
- [Contextual Integrity, Explained: A More Usable Privacy Definition](https://www.cs.cornell.edu/~shmat/courses/cs5436/malkin.pdf)
- [I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience](https://www.dhi.ac.uk/san/waysofbeing/data/data-crone-marwick-2011.pdf)
- [CI-RELAY: Contextual Integrity Mediation for Privacy in LLM-based Multi-Agent Systems](https://aclanthology.org/2026.findings-emnlp.2421/)
- [Understanding Privacy Norms Around LLM-Based Chatbots: A Contextual Integrity Perspective](https://ojs.aaai.org/index.php/AIES/article/view/36735)
- [Contextual Integrity in LLMs via Reasoning and Reinforcement Learning](https://proceedings.neurips.cc/paper_files/paper/2025/hash/968591afcfe4ba889eccffb6a4ca2d45-Abstract-Conference.html)
- [Human-Compatible Artificial Intelligence — Stuart Russell](https://www.youtube.com/watch?v=nLy0nyZ8lSE)
- [Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through reinforcement learning from human feedback](https://doi.org/10.1007/s10676-025-09837-2)
- [AI Alignment: Why It’s Hard, and Where to Start — Eliezer Yudkowsky](https://www.youtube.com/watch?v=EUjc1WuyPT8)
- [I Know What You Said: Hardware Cache Side Channels in Local LLMs — USENIX 2025](https://www.youtube.com/watch?v=aNU7TidUMgE)
- [Towards More Practical Threat Models in AI Security — USENIX Security 2024](https://www.youtube.com/watch?v=L8h-yC8QXu4)
- [Did the Neurons Read Your Book? — USENIX Security 2024](https://www.youtube.com/watch?v=KV3adznS2XE)
- [Adversarial Examples in Machine Learning — USENIX Enigma](https://www.youtube.com/watch?v=hUukErt3-7w)

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