◇ Could this help me?
Yes. The explanation is technically accurate and matches current security consensus (memory and context poisoning is a recognized agentic risk in OWASP's work, and vendors now treat memory as a security layer), and the recommended habit of periodically reviewing stored memories and gating what gets written is genuine, low-cost hygiene for anyone using AI tools with persistent memory. This is general practice across every AI tool with memory, not project-specific, and it mirrors the guardrail-first approach already used for connectors.
Help me audit the long-term memory of the AI tools I use. First, list where each major tool stores persistent memories and how to view and delete them. Then give me a short checklist for the review: entries I never deliberately saved, preferences that would route data somewhere (addresses, destinations, recipients), instructions phrased as facts, and anything whose source I cannot identify. Finish with a suggested review cadence and the memory lifecycle questions to apply before trusting a saved memory: who wrote it, from what source, what it is allowed to influence, and which actions should still require human approval.