Large Language Models (LLMs) are increasingly targeted by sophisticated multi-turn attacks that exploit their conversational nature to bypass security measures. These attacks involve a series of interactions where attackers manipulate the model's responses over multiple turns to extract sensitive information or induce harmful outputs. The complexity of these attacks poses significant challenges for cybersecurity professionals aiming to protect AI-driven systems. This article explores the mechanisms behind multi-turn attacks on LLMs, the potential risks they introduce, and the strategies being developed to mitigate these threats. Understanding these attack vectors is crucial for organizations deploying LLMs to ensure robust defenses against evolving AI threats. The discussion also highlights the importance of continuous monitoring and adaptive security frameworks to safeguard AI applications in various sectors.
This Cyber News was published on www.infosecurity-magazine.com. Publication date: Thu, 06 Nov 2025 15:00:03 +0000