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

TL;DR

The paper targets prompt injection, strategic deception, and bias in enterprise-scale transformers by introducing UTDMF, a generalized patching framework that detects threat-induced activation anomalies and mitigates them through a multi-term loss and real-time filtering. It defines three hypotheses—Threat Chaining (H1), Activation Forecasting (H2), and Inverse Scaling (H3)—and introduces metrics such as the Threat Propagation Index (TPI) and Activation Forecasting, along with the Inverse Scaling Metric (ISM). Across 700+ experiments on Llama-3.1 (405B), GPT-4o, and Claude-3.5, UTDMF achieves 92% detection for prompt injections, 65% reduction in deceptive outputs, and 78% fairness improvements, demonstrating robust performance in production-like settings. An open-source RESTful toolkit enables enterprise integration, with case studies in finance and healthcare and scalable PySpark simulations showing practical viability for real-time threat mitigation. The work points to future directions in adaptive, multimodal defenses, human-in-the-loop oversight, and governance-aligned safety frameworks.

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.