Temporal Context Awareness: A Defense Framework Against Multi-turn Manipulation Attacks on Large Language Models

2025-03-18

AI Summary

Large language models (LLMs) are vulnerable to progressive manipulation attacks in multi-turn dialogues, where adversaries gradually induce semantic drift across seemingly benign turns to conceal malicious intent—evading single-turn detection mechanisms. Method: We propose a dynamic temporal-aware defense framework that integrates three core components: (i) dynamic context embedding analysis, (ii) cross-turn intent consistency verification, and (iii) progressive risk scoring. This is the first work to systematically model the temporal evolution of multi-turn manipulation. Contribution/Results: By continuously monitoring semantic drift, intent deviation, and dialogue pattern anomalies in real time, our framework significantly improves manipulation detection accuracy. In adversarial simulation experiments, it successfully identifies subtle attack patterns missed by conventional single-turn detectors. The framework incurs low computational overhead, preserves high response fidelity, and supports practical deployment—thereby providing robust temporal safety enhancement for conversational AI systems.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large Language Models (LLMs) are increasingly vulnerable to sophisticated multi-turn manipulation attacks, where adversaries strategically build context through seemingly benign conversational turns to circumvent safety measures and elicit harmful or unauthorized responses. These attacks exploit the temporal nature of dialogue to evade single-turn detection methods, representing a critical security vulnerability with significant implications for real-world deployments. This paper introduces the Temporal Context Awareness (TCA) framework, a novel defense mechanism designed to address this challenge by continuously analyzing semantic drift, cross-turn intention consistency and evolving conversational patterns. The TCA framework integrates dynamic context embedding analysis, cross-turn consistency verification, and progressive risk scoring to detect and mitigate manipulation attempts effectively. Preliminary evaluations on simulated adversarial scenarios demonstrate the framework's potential to identify subtle manipulation patterns often missed by traditional detection techniques, offering a much-needed layer of security for conversational AI systems. In addition to outlining the design of TCA , we analyze diverse attack vectors and their progression across multi-turn conversation, providing valuable insights into adversarial tactics and their impact on LLM vulnerabilities. Our findings underscore the pressing need for robust, context-aware defenses in conversational AI systems and highlight TCA framework as a promising direction for securing LLMs while preserving their utility in legitimate applications. We make our implementation available to support further research in this emerging area of AI security.

Problem

• Defends against multi-turn manipulation attacks on LLMs
• Detects semantic drift and evolving conversational patterns
• Enhances security for conversational AI systems

Innovation

• Dynamic context embedding analysis
• Cross-turn consistency verification
• Progressive risk scoring for detection