Temporal Context Awareness (TCA) provides a defense framework against sophisticated multi-turn manipulation attacks where adversaries build context through seemingly harmless conversation to bypass LLM safety guardrails.

• Addresses a critical security gap in LLMs that traditional single-turn detection methods miss
• Leverages temporal patterns in conversations to identify and prevent manipulation attempts
• Represents a significant advancement in protecting LLMs in real-world deployments
• Could help prevent harmful or unauthorized responses that exploit conversational context

This research is vital for Security teams as it tackles an emerging threat vector in AI systems that can lead to unauthorized information disclosure, harmful content generation, or system misuse in production environments.

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