International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF
Volume 74 | Issue 4 | Year 2026 | Article Id. IJCTT-V74I4P107 | DOI : https://doi.org/10.14445/22312803/IJCTT-V74I4P107

Evaluating Attempted Data Manipulation on Generative Artificial Intelligence Reliability

Laura Poe, Dawn Schwartz

Received Revised Accepted Published
02 Mar 2026 30 Mar 2026 21 Apr 2026 30 Apr 2026

Citation :

Laura Poe, Dawn Schwartz, "Evaluating Attempted Data Manipulation on Generative Artificial Intelligence Reliability," International Journal of Computer Trends and Technology (IJCTT), vol. 74, no. 4, pp. 82-91, 2026. Crossref, https://doi.org/10.14445/22312803/IJCTT-V74I4P107

Abstract

Artificial Intelligence (AI) has created a pathway to retrieve and calculate data for generating business plans, performing analyses, and conducting research. The value of AI when used in research is intended to reduce the time frame for collecting data and resources. The detriment of new generative AI tools is the question of reliability. This research evaluated two commonly used tools, ChatGPT and Claude, to determine the ability of the tools to recognize (1) false or contradictory data input into the tools directly from the user prompt and (2) false or contradictory data published on the Internet. The results of the study confirm that ChatGPT and Claude have safeguards to prevent data poisoning through direct chat interactions. However, when the bad dataset was published online to a publicly available website, the generative AI tools had difficulty determining validity, introducing concerns for data reliability. This study demonstrates risks regarding the veracity of AI-generated output and its implications for both research and industry.

Keywords

Data Poisoning, Generative Artificial Intelligence, Content Verification, Data Authenticity, Ai Reliability.

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