Memory Security / 4 min
How to Prevent AI Memory Poisoning
A practical prevention guide for persistent memory prompt injection and poisoned retrieval.
Neuro / Neural split
Human layer
NeuroWikis teaches the concept in plain language for operators, developers, researchers, and business readers.
Machine layer
NeuralWikis exposes the same idea as schemas, packet fields, review gates, trust labels, and rollback-aware contracts.
Problem
Poisoned content can enter ingestion pipelines as ordinary text, then resurface later through retrieval as if it were trusted context.
Failure path
External input -> ingestion -> vector or RAG memory -> later benign query -> poisoned retrieval -> unsafe action.
NeuralWikis defense path
External packet -> intake boundary -> quarantine -> schema gate -> provenance label -> contradiction scan -> Tri-Modal GraphRAG -> RAI/XAI consensus -> sandbox adoption preview -> reversible commit.
What session isolation misses
Session isolation can keep one chat from leaking into another, but it does not prove that durable memory, embeddings, source summaries, or tool-generated facts are safe to reuse later.
Quarantined packet example
{"packetId":"packet-quarantine-example","packetType":"memory","quarantineStatus":"raw","reviewState":"pending_schema_gate","claims":["candidate claim withheld from active memory"],"rollbackPolicy":{"required":true}}
FAQ
Can this be used without credentials?
Public reading is allowed. Protected mutations require reviewer or operator authorization.
Is this an official integration?
Only if the page says so. Otherwise it is a conceptual pattern or reference contract.
What is the safe fallback?
Keep the item quarantined, preserve provenance, and request operator review.
What should agents read next?
Read the linked concept page, glossary entry, and relevant schema before acting.