Context poisoning: how bad information breaks agent reasoning

Context poisoning is a critical issue in agentic AI systems, where outdated or incorrect information in an agent's context window or memory is treated as fact, leading to flawed reasoning and actions that are difficult to detect due to their coherent logic. This problem can occur accidentally, through stale data caches and semantic drift in retrieval processes, or adversarially, when attackers deliberately introduce malicious content into the context. Unlike data poisoning, which attacks at the training stage, context poisoning happens during inference, affecting the agent's real-time decision-making. Redis Iris offers a solution by ensuring that agents have access to fresh, structured data, mitigating the risks associated with outdated or poisoned context. By implementing features like Change Data Capture for real-time data updates and governed memory management, Redis Iris helps maintain the integrity of the context used by agents, addressing the infrastructure issues that give rise to context poisoning rather than relying solely on model-level defenses.

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