New research into ChromaDB memory poisoning demonstrates that attackers no longer need to bypass LLM filters if they can corrupt the 'memory' the AI relies on. By injecting semantically relevant but false data into vector databases, an adversary can steer an AI agent's decisions without a single prompt injection. This 'semantic hijacking' is nearly impossible to detect in standard logs because the poisoned entry looks identical to a legitimate retrieval result. This represents a fundamental threat to the reliability of autonomous AI agents in enterprise workflows.