# A Systematic Literature Review of Information Security in Chatbots
## 2 Method  
Figure 1 shows the literature review methodology used in this study. To ensure a comprehensive and transparent review of the literature, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance in conducting this systematic literature review on chatbots and security. The PRISMA methodology is a widely recognized and accepted approach for conducting systematic literature reviews (SLRs) [30]. It provides a transparent and reproducible framework for conducting literature searches, screening and selecting relevant articles, and synthesizing the findings. In the context of a SLR focused on security threats and vulnerabilities in chatbots, the PRISMA methodology was considered an appropriate approach as it helps to ensure a comprehensive and systematic search of the literature and a rigorous process for screening and selecting relevant articles. Additionally, the PRISMA methodology includes a detailed reporting checklist, which can help ensure that the review is reported in a clear and transparent manner. Figure 1: Literature review methodology. Other approaches to conducting SLRs, such as the Cochrane methodology [31], may also be appropriate for certain research questions or topics. However, in the case of this specific SLR, we may have found that the PRISMA methodology was the most suitable based on its emphasis on transparency and reproducibility. There are 8 sequential steps involved the literature review methodology, and they are as follows:  
Step 1. Defining the research questions and objectives: The research questions and objectives were defined to guide the search and analysis of the literature. The research questions and objectives are as follows:  
Research Questions:  
1. What are the major security threats and vulnerabilities faced by chatbots? 2. What strategies and technologies can be used to mitigate these risks? Objectives:  
1. To provide a comprehensive analysis of the major security threats and vulnerabilities faced by chatbots. 2. To highlight the strategies and technologies that can be used to mitigate these risks. We believe that the findings of this literature review can guide future research and practice in the field of information security in chatbots. Step 2: Identifying relevant databases: Several databases, including the ACM Digital Library, IEEE Xplore, ScienceDirect, and Web of Science, were selected to ensure a comprehensive search of the literature. Step 3. Selecting search terms: The search terms were selected based on the research questions and objectives. The chosen search terms were \"chatbot\" OR \"ChatGPT\" AND \"security\" OR \"information security\". Inclusion Criteria:  
We included peer-reviewed research articles that focused specifically on the security of chatbots and were published between 2016 and 2023. Additionally, we only considered studies that were available in full-text format and published in English. Exclusion Criteria:  
We excluded studies not relating to the security of chatbots, such as those that focused on the development, technology, and applications of chatbots, natural language processing, or user experience. Only peer-reviewed research articles were considered for inclusion in this review. Step 5. Screening and selecting studies: The selected studies were screened based on their relevance to the research questions and objectives. Full-text articles were then reviewed, and studies that did not meet the inclusion criteria or were not relevant to the research questions were excluded. Step 6. Extracting and analyzing data: Relevant data, such as the research methods used, the types of chatbots examined, and the specific security issues addressed, were extracted from the selected studies. The extracted data were then analyzed to identify common themes, patterns, and gaps in the literature. Step 7. Synthesizing the findings: The synthesized findings were presented in a narrative format, with tables and figures used to provide visual representations of the data. Step 8.