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In today's Revolution 4.0 era, the rise of digital technologies contributes significantly to the management of internal processes, ensuring transparency and accountability across all facets of the organization whi...
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Many of us are busy in our lives and may not bother about blind people who need help in their basic tasks like walking. Hence, to make them more independent. In this paper, propose a design of a pair of smart shoe ext...
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Programming assignment source code plagiarism detection is one of the important challenges in intelligent education. This paper provides an overview of program detection techniques in this field, reviews the related l...
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The drug supply chain involves several parties with different roles. Several global pharmaceutical companies, such as drug manufacturers, sell and distribute their products to hospitals and drug distributors in severa...
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Large Language Models (LLMs) have demonstrated effectiveness in tackling coding tasks, leading to their growing popularity in commercial solutions like GitHub Copilot and ChatGPT. These models, however, may be trained...
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ISBN:
(纸本)9783031702440;9783031702457
Large Language Models (LLMs) have demonstrated effectiveness in tackling coding tasks, leading to their growing popularity in commercial solutions like GitHub Copilot and ChatGPT. These models, however, may be trained on proprietary code, raising concerns about potential leaks of intellectual property. A recent study indicates that LLMs can memorize parts of the source code, rendering them vulnerable to extraction attacks. However, it used white-box attacks which assume that adversaries have partial knowledge of the training set. This paper presents a pioneering effort to conduct a black-box attack (reconstruction attack) on an LLM designed for a specific coding task code summarization. The results achieved reveal that while the attack is generally unsuccessful (with an average BLEU score below 0.1), it succeeds in a few instances, reconstructing versions of the code that closely resemble the original.
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