Game-Theoretic Multi-Agent Control for Robust Contextual Reasoning in LLMs
A game-theoretic multi-agent control framework addresses prompt-injection and context-poisoning in multi-turn LLM interactions — a defence pattern enterprises should watch as agentic AI moves into production pipelines.
Summary written by editorial AI · Source link below
arXiv:2606.10322v2 Announce Type: replace Abstract: Large Language Models (LLMs) in multi-turn interactions maintain evolving context rather than generating isolated responses, making them vulnerable to prompt-injection and context-poisoning attacks in which locally plausible adversarial fragments gradually distort reasoning trajectories. Existing defenses mainly filter individual outputs and often ignore context evolution across turns, leaving long-horizon reasoning exposed. Although the Model
Editorial Analysis
As enterprises deploy LLM-based agents in customer-facing and internal workflows, prompt injection remains the most practical attack vector; formal defence frameworks may become essential for EU AI Act conformity assessments.
Evaluate context-integrity controls and prompt-injection defences for any multi-turn LLM deployment before moving to production.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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