| title | Self-State Attacks on Self-Hosted AI Agents: How Far Can OS Defenses Go? |
|---|---|
| authors | Yimeng Chen, Nathanaël Denis, Roberto Di Pietro, Jürgen Schmidhuber |
| date | 2026-07-20 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.17986 |
| affiliation | KAUST Center of Excellence in Generative AI |
| upvotes | 4 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Self-hosted AI agents read and write their own memory and configuration files to function. An agent may get compromised via corruption of its own state -- a compromise realized via legitimate OS system call invocation. We refer to this class of threats as self-state attacks. In this paper, we investigate the OS resilience to this class of attacks. Formally, we characterize a four-axis attack space (Target, Mechanism, Granularity, Temporal); investigate the structural limits of prevention, detection, and recovery; and introduce a workload-conditioned view of detectability. To instantiate the framework, we collect live activity traces from a representative self-hosted agent running across distinct workload profiles, and realize the attack space as a 23-cell matrix, 43 concrete operations on real self-state files, and injected into those traces. We then evaluate both canonical and workload-conditioned defense strategies. The empirical results show that a layered defense stack (access-control prevention on the instruction and configuration layers, workload-conditioned detection on the memory layer, and periodic backup for recovery) is effective on most attack cells while a small residual attack surface remains structurally indistinguishable at the OS level. These findings suggest that against the newly established class of self-state attacks, OS-level defense needs to be reconsidered, potentially opening new research directions in the field. |
cite
@misc{chen2026self,
title={Self-State Attacks on Self-Hosted AI Agents: How Far Can OS Defenses Go?},
author={Yimeng Chen and Nathanaël Denis and Roberto Di Pietro and Jürgen Schmidhuber},
year={2026},
eprint={2607.17986},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.17986}
}