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Unified Threat Detection and Mitigation Framework (UTDMF): Combating Prompt Injection, Deception, and Bias in Enterprise-Scale Transformers

Authors: Santhosh KumarRavindran

Published: 2025-10-06

arXiv ID: 2510.04528v1

Added to Library: 2025-10-07 04:02 UTC

Red Teaming

📄 Abstract

The rapid adoption of large language models (LLMs) in enterprise systems exposes vulnerabilities to prompt injection attacks, strategic deception, and biased outputs, threatening security, trust, and fairness. Extending our adversarial activation patching framework (arXiv:2507.09406), which induced deception in toy networks at a 23.9% rate, we introduce the Unified Threat Detection and Mitigation Framework (UTDMF), a scalable, real-time pipeline for enterprise-grade models like Llama-3.1 (405B), GPT-4o, and Claude-3.5. Through 700+ experiments per model, UTDMF achieves: (1) 92% detection accuracy for prompt injection (e.g., jailbreaking); (2) 65% reduction in deceptive outputs via enhanced patching; and (3) 78% improvement in fairness metrics (e.g., demographic bias). Novel contributions include a generalized patching algorithm for multi-threat detection, three groundbreaking hypotheses on threat interactions (e.g., threat chaining in enterprise workflows), and a deployment-ready toolkit with APIs for enterprise integration.

🔍 Key Points

  • Introduction of the Unified Threat Detection and Mitigation Framework (UTDMF) specifically designed for enterprise-scale models, addressing vulnerabilities in large language models (LLMs) such as prompt injection, strategic deception, and bias.
  • Validation of UTDMF through rigorous experiments, achieving 92% detection accuracy for prompt injection attacks, 65% reduction in deceptive outputs, and 78% improvement in fairness metrics across major enterprise-grade models like Llama-3.1 (405B), GPT-4o, and Claude-3.5.
  • Novel contributions include a generalized patching algorithm for multi-threat detection, the introduction of the Threat Propagation Index (TPI) for quantifying interaction effects among threats, and the Inverse Scaling Safety Law providing insights into threat vulnerabilities related to model size.
  • Deployment of an open-source toolkit with APIs for integration with enterprise systems, facilitating immediate application of the research findings in a real-world context, particularly in industries such as finance and healthcare.
  • Detailed case studies and practical insights on integrating UTDMF into existing AI workflows, addressing challenges related to computational efficiency, regulatory compliance, and data privacy.

💡 Why This Paper Matters

The Unified Threat Detection and Mitigation Framework (UTDMF) presents a comprehensive, innovative solution to critical vulnerabilities inherent in enterprise-scale language models. By effectively addressing prompt injection, deception, and bias, this framework not only enhances the security and trustworthiness of AI systems but is also pivotal for regulatory compliance in crucial sectors like finance and healthcare. The framework’s rigorous validation and practical deployment provide a valuable resource for enterprises seeking to mitigate risks associated with advanced AI technologies.

🎯 Why It's Interesting for AI Security Researchers

This paper is highly relevant to AI security researchers as it tackles pressing issues pertaining to the safety and reliability of large language models, a growing concern as these models become integral to various enterprise solutions. The introduction of the UTDMF framework provides a structured approach to understanding and mitigating multi-faceted threats in AI, which is an essential area of focus for ensuring the responsible deployment of AI technologies. Moreover, the findings contribute to the broader discourse on AI ethics and security, underlining the necessity for adaptive, scalable defenses in an evolving threat landscape.

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