mlsanityData to 14 Oct 2026

Adversarial Prompting Framework for AI Safety Assessment

arXiv 2607.13453 ↗First posted 15 Jul 2026cs.CR cs.AIcited by 0

On the map: LLM safety and security → Prompt injection and agent security

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security. Builds on 3 of its 13 references that are in the map. First posted on arXiv 15 Jul 2026.

Builds on

3 of 13 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications14 Nov

Other references · 2

  1. 2025SHADE-Arena: Evaluating Sabotage and Monitoring in LLM Agentscited by 71
  2. 2024Securing Large Language Models: Threats, Vulnerabilities and Responsible Practicescited by 37

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  1. 2025Computational Safety for Generative AI: A Hypothesis Testing Perspective
  2. 2025GenAI Security: Outsmarting the Bots with a Proactive Testing Framework
  3. 2025Offensive Security for AI Systems: Concepts, Practices, and Applications
  4. 2024A Formal Framework for Assessing and Mitigating Emergent Security Risks in Generative AI Models: Bridging Theory and Dynamic Risk Mitigation
  5. 2026STRIDE-AI: A Threat Modeling Framework for Generative AI Security Assessment
  6. 2023Robust Safety Classifier for Large Language Models: Adversarial Prompt Shield
  7. 2026When Prompt Optimization Becomes Jailbreaking: Adaptive Red-Teaming of Large Language Models
  8. 2024Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI