arXiv AI August 6, 2026 By Prashant Kulkarni, Assaf Namer

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

arXiv:2503. 15560v1 Announce Type: cross 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 18

TRACE: Trajectory Aware Reasoning for Multi-Turn Adversarial Conversation Evaluation

arXiv:2608. 15594v1 Announce Type: new Abstract: Multi-turn jailbreak attacks have emerged as a critical safety threat to LLMs, as harmful objectives are decomposed across a sequence of apparently benign turns to bypass guardrails.

By Md Messal Monem Miah, Adrita Anika, Zhiyuan Yu, Ruihong Huang

llms robotics fine-tuning multimodal benchmarks safety

arXiv AI

Jul 14

Trojan Horse Prompting: Jailbreaking Conversational Multimodal Models by Forging Assistant Message

arXiv:2507. 04673v2 Announce Type: replace Abstract: The rise of conversational interfaces has greatly enhanced LLM usability by leveraging dialogue history for sophisticated reasoning.

By Wei Duan, Li Qian

llms multimodal safety

arXiv AI

Jul 14

A Low-Latency Fraud Detection Layer for Detecting Adversarial Interaction Patterns in LLM-Powered Agents

arXiv:2605. 01143v2 Announce Type: replace Abstract: Large Language Model (LLM)-powered agents demonstrate strong capabilities in autonomous task execution, tool use, and multi-step reasoning.

By Sheldon Yu, Yingcheng Sun, Hanqing Guo, Qianqian Tong

llms agents benchmarks safety

arXiv Machine Learning

Aug 4

Turn-Based Structural Triggers: Structure-Conditioned Backdoors in Multi-Turn LLMs

arXiv:2601. 14340v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed as multi-turn assistants and customized through instruction tuning with project-specific training components.

By Yiyang Lu, Jinwen He, Yue Zhao, Kai Chen, Ruigang Liang, Cheng Hong, Yingjun Zhang

llms fine-tuning

arXiv AI

Aug 11

Automating Deception: Scalable Multi-Turn LLM Jailbreaks

arXiv:2511. 19517v3 Announce Type: replace-cross Abstract: Multi-turn conversational attacks, which leverage psychological principles like Foot-in-the-Door (FITD), where a small initial request paves the way for a more significant one, to bypass safety alignments, pose a persistent threat to Large Language Models (LLMs).

By Adarsh Kumarappan, Ananya Mujoo

llms robotics multimodal benchmarks safety

arXiv AI

Jun 19

Analyzing Defensive Misdirection Against Model-Guided Automated Attacks on Agentic AI Systems

arXiv:2606. 20470v1 Announce Type: cross Abstract: Agentic AI systems increasingly rely on language-model components to interpret instructions, process external data, invoke tools, and coordinate with other agents.

By Reza Soosahabi, Vivek Namsani

agents multimodal benchmarks safety

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Related stories

TRACE: Trajectory Aware Reasoning for Multi-Turn Adversarial Conversation Evaluation

Trojan Horse Prompting: Jailbreaking Conversational Multimodal Models by Forging Assistant Message

A Low-Latency Fraud Detection Layer for Detecting Adversarial Interaction Patterns in LLM-Powered Agents

Turn-Based Structural Triggers: Structure-Conditioned Backdoors in Multi-Turn LLMs

Automating Deception: Scalable Multi-Turn LLM Jailbreaks

Analyzing Defensive Misdirection Against Model-Guided Automated Attacks on Agentic AI Systems