Adversarial Prompting Framework for AI Safety Assessment

2026-07-15

AI Summary

This study addresses emerging security threats such as adversarial prompt attacks (APAs) that challenge the robustness of generative AI systems in enterprise deployments, necessitating systematic evaluation frameworks. To this end, the paper proposes the Adversarial Prompt Framework (APF), the first multi-level, structured methodology for generating and evaluating adversarial prompts across diverse attack vectors—from direct harmful requests to sophisticated encoded attacks. APF integrates automated prompt generation, encoding transformations, and quantitative safety metrics to enable end-to-end security assessment. Experimental results demonstrate significant variability in model vulnerability across attack types, with encoded prompts achieving the highest success rates in bypassing built-in safety mechanisms, thereby validating APF’s effectiveness and practical utility for comprehensive robustness evaluation.

Abstract

Artificial Intelligence (AI), especially Generative AI (GenAI), adoption has increased in industries significantly in recent years. However, the use of these models may also expose systems to new forms of cyberattacks by different malicious actors -- adversarial prompt attack (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 the model vulnerabilities across different attack vectors, with encoded prompts presenting the highest success rates in bypassing safety mechanisms.

Problem

• Adversarial Prompting
• AI Safety
• Generative AI
• Security Assessment
• Prompt Injection

Innovation

• Adversarial Prompting Framework
• AI Safety Assessment
• Generative AI
• Adversarial Prompt Attack
• Automated Security Testing