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Michael-Andrei Panaitescu-Liess

6 accepted papers

2026

AdvBDGen: A Robust Framework for Generating Adaptive and Stealthy Backdoors in LLM Alignment

AAAI 2026technical

With the increasing adoption of reinforcement learning with human feedback (RLHF) to align large language models (LLMs), the risk of backdoor installation during the alignment process has grown, potentially leading to unintended and harmful behaviors. Existing backdoor attacks mostly focus on simple

Cited by 0SourcePDFScholar
2025

Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?

AAAI 2025technical

Large Language Models (LLMs) have demonstrated impressive capabilities in generating diverse and contextually rich text. However, concerns regarding copyright infringement arise as LLMs may inadvertently produce copyrighted material. In this paper, we first investigate the effectiveness of watermark…

Cited by 4SourcePDFScholar
2025

PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models

NAACL 2025long

As the capabilities of large language models (LLMs) continue to expand, their usage has become increasingly prevalent. However, as reflected in numerous ongoing lawsuits regarding LLM-generated content, addressing copyright infringement remains a significant challenge. In this paper, we introduce Po…

Cited by 1SourcePDFScholar
2024

Like Oil and Water: Group Robustness Methods and Poisoning Defenses May Be at Odds

ICLR 2024poster

Group robustness has become a major concern in machine learning (ML) as conventional training paradigms were found to produce high error on minority groups. Without explicit group annotations, proposed solutions rely on heuristics that aim to identify and then amplify the minority samples during tra…

Cited by 0SourcePDFScholar
2024

PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts

ICLR 2024poster

Vision-language models like CLIP are widely used in zero-shot image classification due to their ability to understand various visual concepts and natural language descriptions. However, how to fully leverage CLIP's unprecedented human-like understanding capabilities to achieve better performance is…

2021

Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes

NeurIPS 2021poster

Quantization is a popular technique that transforms the parameter representation of a neural network from floating-point numbers into lower-precision ones (e.g., 8-bit integers). It reduces the memory footprint and the computational cost at inference, facilitating the deployment of resource-hungry m…