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Security and Authenticity of AI-generated code

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Titel: Security and Authenticity of AI-generated code
Autoren: Ambati, Sriharitha
Weitere Verfasser: Stakhanova, Natalia, Eager, Derek, Roy, Chanchal
Publikationsjahr: 2023
Bestand: University of Saskatchewan: eCommons@USASK
Schlagwörter: Artificial Intelligence, AI-generated code, Common Weakness Enumeration(CWE), Common Vulnerabilities and Exposures, Plagiarism detection, Code attribution
Beschreibung: The intersection of security and plagiarism in the context of AI-generated code is a critical theme through- out this study. While our research primarily focuses on evaluating the security aspects of AI-generated code, it is imperative to recognize the interconnectedness of security and plagiarism concerns. On the one hand, we do an extensive analysis of the security flaws that might be present in AI-generated code, with a focus on code produced by ChatGPT and Bard. This analysis emphasizes the dangers that might occur if such code is incorporated into software programs, especially if it has security weaknesses. This directly affects developers, advising them to use caution when thinking about integrating AI-generated code to protect the security of their applications. On the other hand, our research also covers code plagiarism. In the context of AI-generated code, plagiarism, which is defined as the reuse of code without proper attribution or in violation of license and copyright restrictions, becomes a significant concern. As open-source software and AI language models proliferate, the risk of plagiarism in AI-generated code increases. Our research combines code attribution techniques to identify the authors of AI-generated insecure code and identify where the code originated. Our research emphasizes the multidimensional nature of AI-generated code and its wide-ranging repercussions by addressing both security and plagiarism issues at the same time. This complete approach adds to a more profound understanding of the problems and ethical implications associated with the use of AI in code generation, embracing both security and authorship-related concerns.
Publikationsart: thesis
Dateibeschreibung: application/pdf
Sprache: English
Relation: https://hdl.handle.net/10388/15154
Verfügbarkeit: https://hdl.handle.net/10388/15154
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  Data: Security and Authenticity of AI-generated code
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  Data: <searchLink fieldCode="AR" term="%22Ambati%2C+Sriharitha%22">Ambati, Sriharitha</searchLink>
– Name: Author
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  Data: Stakhanova, Natalia<br />Eager, Derek<br />Roy, Chanchal
– Name: DatePubCY
  Label: Publication Year
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  Data: 2023
– Name: Subset
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  Data: University of Saskatchewan: eCommons@USASK
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22AI-generated+code%22">AI-generated code</searchLink><br /><searchLink fieldCode="DE" term="%22Common+Weakness+Enumeration%28CWE%29%22">Common Weakness Enumeration(CWE)</searchLink><br /><searchLink fieldCode="DE" term="%22Common+Vulnerabilities+and+Exposures%22">Common Vulnerabilities and Exposures</searchLink><br /><searchLink fieldCode="DE" term="%22Plagiarism+detection%22">Plagiarism detection</searchLink><br /><searchLink fieldCode="DE" term="%22Code+attribution%22">Code attribution</searchLink>
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  Label: Description
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  Data: The intersection of security and plagiarism in the context of AI-generated code is a critical theme through- out this study. While our research primarily focuses on evaluating the security aspects of AI-generated code, it is imperative to recognize the interconnectedness of security and plagiarism concerns. On the one hand, we do an extensive analysis of the security flaws that might be present in AI-generated code, with a focus on code produced by ChatGPT and Bard. This analysis emphasizes the dangers that might occur if such code is incorporated into software programs, especially if it has security weaknesses. This directly affects developers, advising them to use caution when thinking about integrating AI-generated code to protect the security of their applications. On the other hand, our research also covers code plagiarism. In the context of AI-generated code, plagiarism, which is defined as the reuse of code without proper attribution or in violation of license and copyright restrictions, becomes a significant concern. As open-source software and AI language models proliferate, the risk of plagiarism in AI-generated code increases. Our research combines code attribution techniques to identify the authors of AI-generated insecure code and identify where the code originated. Our research emphasizes the multidimensional nature of AI-generated code and its wide-ranging repercussions by addressing both security and plagiarism issues at the same time. This complete approach adds to a more profound understanding of the problems and ethical implications associated with the use of AI in code generation, embracing both security and authorship-related concerns.
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      – Text: English
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: AI-generated code
        Type: general
      – SubjectFull: Common Weakness Enumeration(CWE)
        Type: general
      – SubjectFull: Common Vulnerabilities and Exposures
        Type: general
      – SubjectFull: Plagiarism detection
        Type: general
      – SubjectFull: Code attribution
        Type: general
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      – TitleFull: Security and Authenticity of AI-generated code
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            NameFull: Ambati, Sriharitha
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            NameFull: Stakhanova, Natalia
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            NameFull: Roy, Chanchal
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              Y: 2023
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