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

arXiv: https://arxiv.org/abs/2503.15560 · HTML: https://mlsanity.com/paper/2503.15560/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 2 of 2)

- [LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet](https://mlsanity.com/paper/2408.15221/) (2024)
- [Speak Out of Turn: Safety Vulnerability of Large Language Models in Multi-turn Dialogue](https://mlsanity.com/paper/2402.17262/) (2024)

## Built on by (most cited first, 5 of 5)

- [Web Intellectual Property at Risk: Preventing Unauthorized Real-Time Retrieval by Large Language Models](https://mlsanity.com/paper/2505.12655/) (2025)
- [MT-JailBench: A Modular Benchmark for Understanding Multi-Turn Jailbreak Attacks](https://mlsanity.com/paper/2605.11002/) (2026)
- [D-Judge: Disrupting Multi-Turn Jailbreaks using Semantics-Preserving Output Rewriting](https://mlsanity.com/paper/2606.02640/) (2026)
- [Cognitive Firewall: A Proactive, Zero-Trust, Multi-Gate Framework for LLM Safety](https://mlsanity.com/paper/2607.01277/) (2026)
- [On Identifying Adversarial Intent Injection in AI-Native 6G Networks](https://mlsanity.com/paper/2609.12144/) (2026)

## Abstract (arXiv)

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.