most citedDigger: Detecting Copyright Content Mis-usage in Large Language Model Training

6 citations · 13 across the 5 of their papers we have counts for

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cs.CR2024

Efficient Detection of Toxic Prompts in Large Language Models

Yi Liu, Junzhe Yu, Huijia Sun +4

Large language models (LLMs) like ChatGPT and Gemini have significantly advanced natural language processing, enabling various applications such as chatbots and automated content g…

cs.CR2024

Oedipus: LLM-enchanced Reasoning CAPTCHA Solver

Gelei Deng, Haoran Ou, Yi Liu +3

CAPTCHAs have become a ubiquitous tool in safeguarding applications from automated bots. Over time, the arms race between CAPTCHA development and evasion techniques has led to incr…

cs.CR2024

Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues

Zhiyuan Chang, Mingyang Li, Yi Liu +3

With the development of LLMs, the security threats of LLMs are getting more and more attention. Numerous jailbreak attacks have been proposed to assess the security defense of LLMs…

cs.CR20244 cited

Pandora: Jailbreak GPTs by Retrieval Augmented Generation Poisoning

Gelei Deng, Yi Liu, Kailong Wang +3

Large Language Models~(LLMs) have gained immense popularity and are being increasingly applied in various domains. Consequently, ensuring the security of these models is of paramou…

cs.CR2024

MiniScope: Automated UI Exploration and Privacy Inconsistency Detection of MiniApps via Two-phase Iterative Hybrid Analysis

Shenao Wang, Yuekang Li, Kailong Wang +4

The advent of MiniApps, operating within larger SuperApps, has revolutionized user experiences by offering a wide range of services without the need for individual app downloads. H…

cs.CR20246 cited

Digger: Detecting Copyright Content Mis-usage in Large Language Model Training

Haodong Li, Gelei Deng, Yi Liu +7

Pre-training, which utilizes extensive and varied datasets, is a critical factor in the success of Large Language Models (LLMs) across numerous applications. However, the detailed…