MeritSKU ingests untrusted product descriptions, supplier copy, and customer reviews. To prevent malicious third parties from hijacking extraction models, all untrusted inputs are wrapped within randomized 128-bit hex nonces and sanitized across 17 distinct adversarial attack vectors.
Cited Claim IDs:PUB-CLM-018
1. The 17 Neutralized Attack Vectors
Our sanitizer neutralizes 17 adversarial injection techniques:
- System prompt overrides & jailbreaks ("Ignore previous instructions...")
- Markdown image exfiltration payloads ()
- XML delimiter escape attempts (</system>, </document>)
- Zero-width unicode control characters and homoglyph substitution
- CSV formula injections (=CMD|... or @SUM...)
- Multi-language instruction bypasses (Spanish, French, German, Mandarin)
- Bidirectional text overrides (RLO/LRO unicode characters)
2. Random 128-Bit Hex Nonce Framing
Untrusted content is framed within unique, unpredictable nonces. The LLM is instructed to treat everything inside the nonce boundary strictly as passive string data:
MARKDOWNnonce-prompt-structure.txt
<untrusted_content_a7f92b4c10e83d6a9e5b2f8c14d70a3e>
User supplier text inserted here without chance of escaping boundary
</untrusted_content_a7f92b4c10e83d6a9e5b2f8c14d70a3e>3. Fail-Closed JSON Sanitization
All model outputs are parsed through strict Zod schemas with 0.0 temperature. Any unparseable or malicious output fails closed to typed null with zero data persistence.
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