Hamid Mozaffari
I am an AI research scientist at Oracle, where I develop methods for auditing the security, privacy, and alignment of large language models and agentic systems. My research includes automated red teaming, reinforcement-learning-based evaluation, memorization and membership-inference auditing, access-control evaluation, and content-moderation robustness. I am particularly interested in building rigorous and scalable evaluation frameworks that uncover hidden failures in deployed AI systems.
I earned my Ph.D. in Computer Science from University of Massachusetts Amherst, where I was advised by Professor Amir Houmansadr. My doctoral research focused on the security, privacy, fairness, and robustness of federated learning.
News #
- Jul 2026 Auditing Moderation Robustness Under Realistic Settings accepted to COLM 2026.
- Sep 2025 Permissioned LLMs: Enforcing Access Control in Large Language Models accepted to NeurIPS 2025.
- Oct 2024 Semantic Membership Inference Attack against Large Language Models presented at the RedTeaming GenAI workshop, NeurIPS 2024.
- Jun 2024 Fake or Compromised? Making Sense of Malicious Clients in Federated Learning accepted to ESORICS 2024.
- Jun 2023 Joined Oracle as a Research Scientist after completing my Ph.D. at University of Massachusetts Amherst.
- Apr 2023 Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks accepted to USENIX Security 2023.
- Apr 2023 Defended my Ph.D. dissertation at University of Massachusetts Amherst.
Research interests #
- LLM security & alignment auditing
- Automated red teaming
- Membership inference & memorization measurement
- Access-control enforcement in LLMs
- Content-moderation robustness
- RL-based evaluation & adversarial optimization
Selected publications #
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2026
Auditing Moderation Robustness Under Realistic Settings
Conference on Language Modeling (COLM), 2026 · *Equal contribution
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2025
Permissioned LLMs: Enforcing Access Control in Large Language Models
Neural Information Processing Systems (NeurIPS), 2025
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2024
Semantic Membership Inference Attack against Large Language Models
RedTeaming GenAI Workshop, NeurIPS 2024
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2024
Fake or Compromised? Making Sense of Malicious Clients in Federated Learning
European Symposium on Research in Computer Security (ESORICS), 2024
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2023
Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks
32nd USENIX Security Symposium, 2023
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2022
FedPerm: Private and Robust Federated Learning by Parameter Permutation
FL Workshop, NeurIPS 2022
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2022
Equity and Equality in Fair Federated Learning
ICML 2022 Workshop on Responsible Decision Making in Dynamic Environments (RDMDE)
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2022
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2020
Heterogeneous Private Information Retrieval
Network and Distributed System Security Symposium (NDSS), 2020
For the full list, please refer to my Google Scholar page.