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Found 7 papers, 1 papers with code
Prompt Stealing Attacks Against Large Language Models
Our proposed prompt stealing attack aims to steal these well-designed prompts based on the generated answers.
Conversation Reconstruction Attack Against GPT Models
We then introduce two advanced attacks aimed at better reconstructing previous conversations, specifically the UNR attack and the PBU attack.
Comprehensive Assessment of Toxicity in ChatGPT
Moderating offensive, hateful, and toxic language has always been an important but challenging topic in the domain of safe use in NLP.
From Visual Prompt Learning to Zero-Shot Transfer: Mapping Is All You Need
In this sense, we propose SeMap, a more effective mapping using the semantic alignment between the pre-trained model's knowledge and the downstream task.
Fine-Tuning Is All You Need to Mitigate Backdoor Attacks
Backdoor attacks represent one of the major threats to machine learning models.
DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models
To tackle this problem, we pioneer a systematic study on the detection and attribution of fake images generated by text-to-image generation models.
Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders
Self-supervised representation learning techniques have been developing rapidly to make full use of unlabeled images.
