WebMEM™

The Protocol for Structuring, Delivering, and Conditioning Trust-Scored AI Memory on the Open Web

Part 17: WebMEM as AI Poisoning Defense

Using Fragment-Level Provenance to Prevent and Recover from AI Misinformation

17.1 Introduction: Poison at the Memory Layer

As retrieval-based AI systems dominate knowledge delivery, they introduce a new surface of risk:
Data poisoning at ingestion scale.

Whether it’s misinformation, emergent bias, misaligned definitions, or adversarial prompt influence—poisoned knowledge embeds deep into model memory, where it silently contaminates generative output.

Most AI systems try to detect poison after it surfaces.
WebMEM prevents poison from surviving ingestion.

17.2 The Limits of Internal Detection

Even with anomaly classifiers, adversarial filters, and red teaming, LLMs can’t fully detect memory poisoning internally.

Why?

  • Poisoned facts can mimic source tone
  • Incorrect definitions can become dominant through reinforcement
  • Hallucinated trust signals can pass unnoticed

Traditional AI defenses scan output.
WebMEM guards the memory layer itself—before hallucination happens.

17.3 Externalized Trust via ProvenanceBlock

WebMEM uses Semantic Digests that include a ProvenanceBlock—a machine-verifiable trust layer embedded in every fragment.

It includes:

  • prov:wasDerivedFrom — Source dataset or publication
  • dcat:license — Usage permissions
  • trust_layer — Confidence level or review status
  • data-confidence — Optional score
  • prov:generatedAtTime — Timestamp of derivation
  • retrieval_format — Output method (JSON-LD, TTL, Markdown, etc.)

This externalizes trust—from model internals to machine-readable content scaffolds.

AI systems, agents, or validators can independently verify, cite, or reject content based on the attached trust metadata.

17.4 Fragment-Level Trust Immunity

Each field in a digest is modular and annotated with:

  • data-id — Unique fragment key
  • derived-from — Source file or dataset
  • defined_term — Glossary alignment
  • derived_translation — Any transformation logic
  • confidence — Optional trust score

This structure enables granular fault tolerance.

If one field is poisoned (e.g., wrong MOOP value), the system can flag or suppress that field…
without discarding the entire digest.

This is structured trust immunity—the AI equivalent of selective rollback.
Memory becomes degradable, not destructible.

17.5 Recovery Through Feedback Loops

Most AI poisoning leads to system-wide instability.
WebMEM enables structured recovery via:

  • Versioned Digests – New entries can supersede old ones, leaving an audit trail
  • TrustScore Decay – Fragment-level retrieval behavior informs when trust is weakening
  • Feedback-Based Reconditioning – Retrieval feedback loops (see Part 9) can identify and repair compromised fragments
  • Machine-Readable Correction Notices – PROV metadata can log retroactive corrections (e.g., prov:invalidatedAtTime, prov:alternateOf)

This turns memory from a black box…
Into a declarative trust graph—auditable, updatable, defensible.

17.6 Summary

WebMEM isn’t just about retrievability.
It’s about resilience.

With structured provenance, versioning, and fault-tolerant memory scaffolds, you create:

  • Trust zones within memory
  • Immunity layers around misinformation
  • Recovery paths for poisoned systems

In a world where AI outputs shape real-world decisions, you don’t just need content that can be retrieved.
You need memory that can be trusted, updated, and defended.