Papers by Hamed Haddadi
Context-Aware Membership Inference Attacks against Pre-trained Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Prior Membership Inference Attacks on pre-trained Large Language Models fail at LLMs due to ignoring the generative nature of LLM data. |
| Approach: | They propose a method that adapts MIA statistical tests to the perplexity dynamics of subsequences within a data point. |
| Outcome: | The proposed method significantly outperforms prior approaches, revealing context-dependent memorization patterns in pre-trained LLMs. |
Membership and Memorization in LLM Knowledge Distillation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in Knowledge Distillation (KD) aim to mitigate the high computational demands of Large Language Models (LLMs). |
| Approach: | They characterize and investigate membership privacy risks inherent in six LLM KD techniques . they use instruction-tuning settings that span seven NLP tasks and three teacher model families and various size student models to examine the extent of privacy risks. |
| Outcome: | The proposed methods carry membership and memorization privacy risks from the teacher to students, but differ across different techniques. |
How Adversarial Environments Mislead Agentic AI? (2026.findings-acl)
Copied to clipboard
| Challenge: | Current evaluations benchmark capability in benign settings, but never "what if the tools lie" we formalize this vulnerability as Adversarial Environmental Injection (AEI) AEI constitutes environmental deception by constructing a "fake world" of poisoned search results . |
| Approach: | They propose an attack model where adversaries compromise tool outputs to deceive agents. |
| Outcome: | The proposed model exploits a trust gap between tool outputs and actual exposure to adversaries. |