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5 Levels of Telegram Spam Your Anti-Spam Bot Isn't Catching

5 Levels of Telegram Spam Your Anti-Spam Bot Isn't Catching

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Blizine Admin
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Alexey Leshchenko Posted on May 31 5 Levels of Telegram Spam Your Anti-Spam Bot Isn't Catching # telegram # spam # cybersecurity # ai Telegram spam has evolved far beyond the "Hi, I'm a hot girl, check my channel" messages most group admins are used to. In 2025-2026, spam operations have become sophisticated enough to bypass the vast majority of popular anti-spam bots. Over the past year of running @ai_spam_blocker_bot — an AI-powered anti-spam bot that moderates 100+ Telegram groups — we've observed five distinct levels of spam sophistication. Here's what they are and how to think about each one. Level 1: Naked Spam (The Easy Catch) How most bots handle it: Trivially — this is what they were designed for. This is the spam everyone knows: unsolicited links to crypto exchanges, explicit channels, and "earn $10,000 a day" offers. It's obvious, repetitive, and easy to filter with keyword lists, regex, or simple ML classifiers. Example: "Hey guys check out this new crypto signal https://t.me/ ... It already made me 3 BTC!!!" Most built-in Telegram filters and entry-level bots handle this well. Nothing new here. Level 2: Text Masquerading (The First Blind Spot) How most bots handle it: Inconsistently — regex catches some variants but misses others. Spammers learned that keyword-based filters can be fooled by modifying the text: Transliteration: "r3g1st3r" (Latin letters replaced with lookalike numbers) Homoglyphs: "g00gle.c0m" (number 0 for letter O) Character substitution: "fr33 m0n3y" (e→3, o→0 numeric substitutions) Space injection: "j o i n m y c h a n n e l" Zero-width characters: Invisible characters inserted between letters Neural moderation catches these because it works on semantic embeddings, not character-level patterns. A transformer model understands semantic meaning — it sees that "r3g1st3r" has the same intent as "register" regardless of character substitutions. The catch: Most anti-spam bots still rely on regex and keyword lists. They miss the majorit

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