Author

Amir Houmansadr

2 papers indexed here

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Preprint Sep 2026

Can Prompt Anonymity Protect Your Identity From LLM Providers?

User conversations with large language models (LLMs) often contain highly sensitive personal information that can be exploited by LLM providers to create detailed user dossiers, enable targeted advertising, and train more powerful models. To protect user privacy, anonymizing LLM proxies have emerged as a practical solu...

Dzung Pham, Dillon Sheils, Naina Singh et al. · 0 citations

#machine learning Feb 2025

OverThink: Slowdown Attacks on Reasoning LLMs

This work evaluates Overthink on proprietary and open-source reasoning models across the FreshQA, SQuAD, and MuSR datasets, and shows that newer generations of RLMs, while showing a drastic increase in per-token cost, also exhibit up to a 2.3x increase in reasoning tokens, leaving them more vulnerable to Overthink atta...

Abhinav Kumar, Jaechul Roh, Ali Naseh et al. · arXiv.org · 92 citations · ⚡9