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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.15205 |
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| _version_ | 1866908780163235840 |
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| author | Hickerson, Don Perkins, Mike |
| author_facet | Hickerson, Don Perkins, Mike |
| contents | This research examines the emerging technique of step-around prompt engineering in GenAI research, a method that deliberately bypasses AI safety measures to expose underlying biases and vulnerabilities in GenAI models. We discuss how Internet-sourced training data introduces unintended biases and misinformation into AI systems, which can be revealed through the careful application of step-around techniques.
Drawing parallels with red teaming in cybersecurity, we argue that step-around prompting serves a vital role in identifying and addressing potential vulnerabilities while acknowledging its dual nature as both a research tool and a potential security threat. Our findings highlight three key implications: (1) the persistence of Internet-derived biases in AI training data despite content filtering, (2) the effectiveness of step-around techniques in exposing these biases when used responsibly, and (3) the need for robust safeguards against malicious applications of these methods.
We conclude by proposing an ethical framework for using step-around prompting in AI research and development, emphasizing the importance of balancing system improvements with security considerations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15205 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Peek Behind the Curtain: Using Step-Around Prompt Engineering to Identify Bias and Misinformation in GenAI Models Hickerson, Don Perkins, Mike Computers and Society This research examines the emerging technique of step-around prompt engineering in GenAI research, a method that deliberately bypasses AI safety measures to expose underlying biases and vulnerabilities in GenAI models. We discuss how Internet-sourced training data introduces unintended biases and misinformation into AI systems, which can be revealed through the careful application of step-around techniques. Drawing parallels with red teaming in cybersecurity, we argue that step-around prompting serves a vital role in identifying and addressing potential vulnerabilities while acknowledging its dual nature as both a research tool and a potential security threat. Our findings highlight three key implications: (1) the persistence of Internet-derived biases in AI training data despite content filtering, (2) the effectiveness of step-around techniques in exposing these biases when used responsibly, and (3) the need for robust safeguards against malicious applications of these methods. We conclude by proposing an ethical framework for using step-around prompting in AI research and development, emphasizing the importance of balancing system improvements with security considerations. |
| title | A Peek Behind the Curtain: Using Step-Around Prompt Engineering to Identify Bias and Misinformation in GenAI Models |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2503.15205 |