arXiv:2606. 30755v1 Announce Type: cross Abstract: Claw-like AI agents (e.
By Peizhi Niu, Wenjie Qu, Shangding Gu, Tianneng Shi, Yuankai Li, Ahmad Tawaha, Hend Alzahrani, Vincent Siu, Boyi Li, Chenguang Wang, Jiaheng Zhang, Basel Alomair, Ming Jin, Muhao Chen, Chi Wang, Costas Spanos, Dawn Song
arXiv:2601. 09923v3 Announce Type: replace Abstract: AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior.
By Hanna Foerster, Tom Blanchard, Kristina Nikoli\'c, Ilia Shumailov, Cheng Zhang, Robert Mullins, Nicolas Papernot, Florian Tram\`er, Yiren Zhao
The paper discusses how large language model agents now act as privileged principals with kernel‑grade authority, yet lack the trusted mediation traditionally required for operating‑system security. It introduces a taxonomy that distinguishes between provenance‑based deterministic checks and content‑semantic checks, identifying a central mediation gap in distinguishing data from instruction and authorized from unauthorized actions. The authors argue that this gap creates an irreducible risk of undetected attacks whenever inputs and actions are not pre‑enumerated, and they propose defenses across runtime monitoring, architectural separation, and authorization while critiquing current evaluation practices. They extend the analysis to AI‑native operating systems where the model itself serves as the arbitration core, outlining design constraints, challenges, and a research agenda.
By Li Zhang, Yang Sun, Jie Shi
arXiv:2606. 27567v1 Announce Type: cross Abstract: Prompt injection is the top security risk for LLM-integrated applications, yet every defense proposed so far has been broken.
By Dewank Pant, Shruti Lohani, Avijit Kumar
arXiv:2602. 20064v2 Announce Type: replace-cross Abstract: Large language models are increasingly deployed as agents: they plan, call tools, read untrusted data, and act on the results.
By Zac Garby, Andrew D. Gordon, David Sands
arXiv:2608. 11274v1 Announce Type: cross Abstract: The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI.
By Albus W. Ng, Yi Han, Jusheng Zhang, Wenhao Wang