How to Ban by Device ID Using Bots (Advanced Telegram Moderation)

• 12 June
• 359 Views

In large Telegram groups or channels, admins sometimes wish to ban repeat offenders even if they attempt to rejoin with alternate accounts. The idea of “banning by device ID” sounds like an effective deterrent, but Telegram’s Bot API does not expose users’ device IDs. This article explains why direct device-based bans aren’t supported and outlines advanced workarounds—using Telegram Web Apps, fingerprinting techniques, and session-tracking strategies—to approximate device-level moderation.

1. Why Direct Device-ID Banning Isn’t Available

• Privacy & API Limitations: Telegram’s Bot API and even user-client libraries (Telethon, Pyrogram) do not provide other users’ device identifiers (IMEI, hardware IDs, or OS-level device tokens). User privacy and security are priorities.
• Bot-Level Scope: Bots only receive updates tied to user_id or chat identifiers. There’s no field for device fingerprint or IP address in message updates.
• Session Info Restrictions: Even with user-authorized libraries, you can inspect your own sessions, not those of other users. You cannot query Telegram for another user’s active sessions or device metadata.

2. High-Level Workaround: User Fingerprinting via Telegram Web Apps

Although you can’t ban by OS-level device ID, you can approximate “device-based” checks by using a Telegram Web App (bot-integrated) to collect browser or environment fingerprints when users interact with your group. This technique helps flag repeat offenders who use the same browser or environment to rejoin with new accounts.

1. Create a Telegram Bot & Web App:

   Use Bot API’s Web App feature. Host a simple HTTPS web page that’s opened via a bot button or deep link. When a user clicks “Verify” or similar, Telegram passes user_id along with an OAuth-like login token to your Web App.

2. Collect Fingerprint Data:

   On the Web App page, run JavaScript to gather non-invasive fingerprint data, e.g.:

   • User-Agent string
   • Screen resolution
   • Browser plugins or features (via feature-detection, not invasive APIs)
   • Timezone and locale
   • Local storage or IndexedDB identifiers (e.g., store a random UUID on first visit)

   Combine these into a hashed “fingerprint”. Store this mapping: fingerprint_hash → Telegram user_id in your database.

3. On Join or Rejoin, Prompt Verification:

   When a new user joins the group, have a bot auto-send a message with a “Verify Device” button leading to your Web App. If the fingerprint matches a previously flagged hash, you can take action (warn or auto-ban).

4. Ban by Fingerprint Instead of Device ID:

   When you detect a misbehaving user, record their fingerprint hash in a “banned fingerprints” list. If a new account triggers the same fingerprint, the bot can automatically restrict or ban that user_id.

3. Alternative Approach: Link-Based & Invite Token Controls

Another way to limit repeat offenders is via per-invite token tracking:

1. Create Unique Invite Links:

   Generate a unique invite link for each user or cohort. Store which link was used by which user_id.

2. Track Link Reuse:

   If a link is used by multiple accounts or flagged users, you can disable that invite link. This prevents the same invite circulating among alternate accounts on the same device or network.

3. Combine with Fingerprinting:

   When a new join occurs via a suspect invite link, also prompt Web App fingerprinting. A match can trigger auto-ban.

4. Session-Based Monitoring via Self-Hosted User Clients

If you run a self-hosted user client (e.g., Telethon) as an admin account, you can monitor group membership and detect patterns—but you still can’t see remote device IDs. However, you can:

• Log Join Patterns: Record timestamps, IP addresses (only if users hit your external service), or fingerprint data from Web App interactions.
• Correlate Multiple Accounts: Identify clusters of user_ids exhibiting similar behavior (e.g., same join times, similar usernames) to flag potential alternate accounts.

5. Implementing the Bot Workflow

1. Bot Setup & Permissions:

   Ensure your bot is admin in the group with permission to restrict and ban users.

2. Auto-Welcome & Verification Trigger:

   On new_chat_member update, bot sends a private message or group mention asking the user to verify via Web App link.

3. Web App Integration:

   User clicks button → opens Web App → Telegram Login authenticates user_id → JS fingerprint collected → sent to your backend.

4. Backend Logic:

   Compare fingerprint against banned list. If matched, bot issues a kickChatMember or banChatMember request for that user_id.

5. Logging & Alerts:

   Store events (join, fingerprint result, ban action) in a log for audit and manual review. Optionally alert moderators in a private admin chat.

6. Re-Verification for Returning Users:

   If a previously verified user rejoins after leaving, prompt re-verification to update fingerprint in case device or environment changed.

6. Privacy, Legal & Ethical Considerations

• User Consent: Inform users why you collect fingerprint data and how it’s used. Provide a brief privacy notice before verification.
• Data Retention: Store fingerprint hashes (not raw data) and delete old records after a defined period (e.g., 6–12 months) to respect privacy.
• False Positives: Understand that fingerprinting can misidentify benign users (e.g., shared devices). Offer an appeal or manual review process.
• Compliance: Ensure your approach aligns with local data protection laws (e.g., GDPR if users in EU). Use hashed or anonymized data whenever possible.

7. Best Practices to Enhance Effectiveness

• Combine Multiple Signals: Fingerprint data + invite link tracking + behavior patterns yield better accuracy than any single method.
• Rotate Verification Methods: Occasionally update fingerprint algorithm parameters or invite token schemes to stay ahead of savvy offenders.
• Monitor Bot Performance: Ensure the verification bot is responsive; handle errors gracefully to avoid blocking legitimate users.
• Educate Your Community: Explain the verification step’s purpose to members—transparency improves compliance and trust.
• Keep Humans in the Loop: For borderline cases (fingerprint near-match), have moderators manually review before banning to reduce mistakes.

8. When to Use This Approach

This advanced moderation workflow is most useful for:

• Large, invite-only or semi-private groups vulnerable to repeat spam attacks.
• Crypto or high-risk niches where bad actors create multiple accounts to bypass bans.
• Communities where repeat offenders harm engagement or security.

If your group is small or open to public without strict moderation, simpler spam filters and manual bans usually suffice.

Conclusion

Telegram does not natively support banning by device ID via Bot API. However, by leveraging Telegram Web Apps for fingerprinting, invite-link tracking, and behavioral analysis, you can approximate device-based moderation to deter repeat offenders. Implementing a verification workflow requires careful attention to privacy and user experience, but for large or high-risk communities, these advanced techniques can significantly enhance moderation effectiveness.

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