Unicode Normalization API for Spam & LLM Security

시장 진출 전략

MVP 범위 · 1~2주

차별화

실패 가능 요인

자가 반박 — 가장 중요한 신뢰 신호

1. 1Open-source libraries like ftfy or unicodedata may already cover 80% of use cases, making a paid API hard to justify — the differentiation would need to be in the comprehensiveness, the curated rule sets, and the threat intelligence aspect rather than basic normalization.
2. 2The market of companies that both understand this problem and are willing to pay for an API rather than building it themselves may be small — most either ignore it or build a quick internal patch that is good enough.
3. 3Spammers may move beyond Unicode-based smuggling to entirely different techniques, making the normalization API less relevant over time and requiring constant research to stay valuable.

근거 요약

AI가 이 인사이트를 합성한 방법 — 직접 인용 없음

Approximately six commenters discussed the difficulty of handling Unicode edge cases in text processing. One user explicitly noted that stripping non-flag tag sequences sounds simple but requires every app to independently research the massive Unicode space. Another pointed out that sanitizing text before feeding it to spam filters or LLM prompts is an obvious step but the mapping tasks are massive and likely imperfectly implemented. A third user highlighted that spammers exploit legitimate fields like Organization Name in email notifications, turning innocuous metadata into the apparent main content. The discussion confirms both the technical complexity and the gaps in current tooling.