Abstract
In recent years, the adoption of Artificial Intelligence (AI), especially Generative AI (GenAI), has significantly increased across industries. However, the use of these models may also expose systems to new forms of cyberattacks by various malicious actors, with adversarial prompt attacks (APA) being one of the most prominent examples of such threats. This paper presents the implementation of an Adversarial Prompting Framework (APF) for a comprehensive assessment of AI safety.
The framework systematically evaluates the resilience of the AI model through the generation of structured adversarial prompts at multiple sophistication levels, from direct harmful requests to advanced encoding-based attacks. Our implementation demonstrates the practical application of this methodology in enterprise environments, providing automated testing capabilities with quantitative security assessment metrics.
The results indicate significant variations in model vulnerabilities across different attack vectors, with encoded prompts presenting the highest success rates in bypassing safety mechanisms.
Blogger's Review: This study offers a fresh perspective on AI safety by introducing the Adversarial Prompting Framework, which effectively identifies and assesses model vulnerabilities, enhancing the safety of Generative AI. Moving forward, optimizing the application of this framework will be a key area of focus.