Author

G. Suh

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Preprint Aug 2026

PIPES: Securing Agent Perception with Provenance and Priors

Tool-using agents consume external data from sources with different levels of trust, yet tool responses rarely identify who produced each component or what it should convey. We show that this gap enables state-corruption attacks, in which attacker-controlled content makes environmental claims beyond the informational a...

Sanjay Kariyappa, Severin Klingler, G. Suh · 0 citations

#machine learning Preprint Oct 2026

Self-Reflection Fine-Tuning: Enhancing Agent Security against Prompt Injection Attacks from Failure Experience

Large language model (LLM) agents are increasingly deployed in tool-augmented environments, but their reliance on external inputs makes them highly vulnerable to prompt injection attacks that can hijack task objectives. Existing safety alignment methods rely on static expert trajectories or preference optimization, lim...

Zi-Xu Wang, Hao Li, Feng Gao et al. · 0 citations

Preprint Aug 2026

Trusted Hardware Acceleration for Function Secret Sharing

The distributed function accelerator (DFA), a hardware accelerator that targets the dominant primitive in FSS: distributed point function (DPF) generation and evaluation, combines a high-throughput fixed-function engine for AES-based pseudorandom number generation with a lightweight programmable unit for protocol-speci...

Pengzhi Huang, Kiwan Maeng, G. Suh · 0 citations