#multi-turn-attacks News & Analysis

4 articles tagged with #multi-turn-attacks. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBearisharXiv – CS AI · Jun 97/10

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PLAGUE: Plug-and-play framework for Lifelong Adaptive Generation of Multi-turn Exploits

Researchers introduce PLAGUE, a framework for conducting multi-turn jailbreak attacks on Large Language Models through a three-phase approach (Primer, Planner, Finisher). The framework achieves unprecedented attack success rates of 81.4% on OpenAI's o3 and 67.3% on Claude's Opus 4.1, demonstrating significant vulnerabilities in models considered highly resistant to jailbreaking.

🏢 OpenAI🧠 Claude🧠 Opus

AINeutralarXiv – CS AI · Jun 27/10

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THRD: A Training-Free Multi-Turn Defense Framework for Jailbreak Attacks on Large Language Models

Researchers have developed THRD, a training-free defense framework that detects multi-turn jailbreak attacks on large language models by tracking how safety risks accumulate across conversation turns. The system achieves 0.2-4.0% attack success rates while maintaining model utility, addressing a critical vulnerability where attackers exploit conversational dynamics rather than single prompts.

AINeutralarXiv – CS AI · May 17/10

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Latent Adversarial Detection: Adaptive Probing of LLM Activations for Multi-Turn Attack Detection

Researchers demonstrate that multi-turn prompt injection attacks leave detectable signatures in language model activation patterns, achieving 93.8% detection accuracy through analysis of residual stream trajectories. The approach reveals that adversarial attack sequences exhibit distinctive 'restlessness' patterns across model architectures, though detection effectiveness varies significantly when deployed on real-world data.

AIBearisharXiv – CS AI · Apr 147/10

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The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems

Researchers have identified a novel jailbreaking vulnerability in LLMs called 'Salami Slicing Risk,' where attackers chain multiple low-risk inputs that individually bypass safety measures but cumulatively trigger harmful outputs. The Salami Attack framework demonstrates over 90% success rates against GPT-4o and Gemini, highlighting a critical gap in current multi-turn defense mechanisms that assume individual requests are adequately monitored.

🧠 GPT-4🧠 Gemini