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Introduction:
In the shadows of Telegram, where threat actors and extremists believe they operate anonymously, a new vulnerability has been exposed—not in encrypted messages, but in seemingly innocent sticker packs. The TGSpyder tool leverages this flaw, automating the deanonymization of channel creators by reverse-engineering sticker pack metadata, turning a common branding tool into a powerful OSINT weapon for cybersecurity professionals and investigators.
Learning Objectives:
- Understand the technical methodology behind deanonymizing Telegram users via sticker pack IDs.
- Learn to install, configure, and operate the TGSpyder CLI tool for automated Telegram OSINT.
- Master integrating this technique into professional investigative workflows, including proxy chaining and data enrichment.
You Should Know:
1. Installation and Core Configuration of TGSpyder
Step‑by‑step guide explaining what this does and how to use it.
Before any operation, you must establish a secure environment for TGSpyder. This Python-based tool requires API credentials from Telegram’s core platform to interact with its MTProto protocol, mimicking a legitimate client. First, clone the repository and install dependencies.
Clone the repository git clone https://github.com/Darksight-Analytics/tgspyder.git cd tgspyder Install Python dependencies (Python 3.7+ required) pip install -r requirements.txt Core dependency: Telethon
Next, obtain your `api_id` and `api_hash` from https://core.telegram.org/api/obtaining_api_id by creating a Telegram application. Configure TGSpyder by placing these credentials in its config file or passing them as environment variables to establish a secure session.
Example: Running with environment variables (Linux/macOS) export TG_API_ID=YOUR_API_ID export TG_API_HASH="YOUR_API_HASH" Verify installation python tgspyder.py --help
2. Scraping Telegram Entities: Members, Messages, and Links
Step‑by‑step guide explaining what this does and how to use it.
TGSpyder’s primary function is the bulk collection of structured data from groups and channels. This data forms the foundation for network analysis and threat intelligence. Use distinct commands for targeted scraping. For comprehensive collection from a target URL:
Scrape both members and message history from a target tgspyder "https://t.me/target_channel" --members --chats
This creates organized `CSV` files in a dedicated `output/` directory. To discretely harvest invite links for mapping connected communities, use the crawl function:
Extract only invitation links from chat history tgspyder "https://t.me/target_channel" --crawl-invites
Always verify your scraper’s output. Use command-line tools to inspect the generated files:
Check the structure of scraped data (Linux/macOS) head -n 5 output/target_channel_members.csv wc -l output/target_channel_messages.csv Count total messages scraped
3. Deanonymizing Through Sticker Pack Reverse Engineering
Step‑by‑step guide explaining what this does and how to use it.
This is TGSpyder’s flagship capability. Threat actors often create custom sticker packs using personal accounts, leaving a cryptographic fingerprint. The tool automates a reverse-engineering process: it extracts a sticker pack’s numeric ID and applies a 32-bit right shift (>> 32) operation to derive the creator’s Telegram User ID. This operation is mathematically equivalent to integer division by 4,294,967,296 (2^32).
Launch the sticker pack deanonymization module tgspyder --sticker-pack When prompted, input the sticker pack link or ID Example: https://t.me/addstickers/HotCherry
The tool performs the calculation and queries the resolved User ID against the Telegram API. You can replicate the core logic in Python for understanding:
Python snippet demonstrating the core deanonymization logic
sticker_pack_id = 1391391008142393345 Example ID from the pack
creator_user_id = sticker_pack_id >> 32 Right shift by 32 bits
print(f"Potential Creator's User ID: {creator_user_id}") Output: 323958464
This derived User ID is the key. While Telegram’s API doesn’t resolve this ID to a username directly, it can be cross-referenced with services like the `@tgdb_bot` or breach databases such as DARKSIDE by District 4 Labs to potentially map it to a real identity.
4. Operational Security: Using Proxies and Tor
Step‑by‑step guide explaining what this does and how to use it.
To protect your identity and avoid rate-limiting or geoblocking during large-scale investigations, TGSpyder supports SOCKS5 proxies, including Tor. First, ensure Tor is running on its default port.
Start the Tor service (Linux) sudo systemctl start tor Verify Tor is running on port 9050 curl --socks5-hostname 127.0.0.1:9050 https://check.torproject.org/api/ip
Integrate the proxy directly into your TGSpyder command. This routes all Telethon API traffic through the Tor network, anonymizing your source IP.
Execute TGSpyder through a local Tor proxy tgspyder "https://t.me/target_channel" --members --proxy socks5://127.0.0.1:9050
For persistent operations, configure proxy settings in your `tgspyder` configuration file to apply them globally to all sessions.
5. User Intelligence Lookups and Enrichment
Step‑by‑step guide explaining what this does and how to use it.
Beyond groups, TGSpyder can perform targeted user lookups, which is crucial for profiling individuals. You can query using either a known username or a numeric ID.
Lookup by username (with @ symbol) tgspyder --user @username Lookup by numeric User ID (obtained from sticker analysis or other sources) tgspyder --user 123456789
The tool fetches available public information via the API. To enrich the derived User IDs from the sticker deanonymization process, manually query them using bots like `@tgdb_bot` within the Telegram app. The workflow is: Sticker Pack ID -> Bit-Shift -> User ID -> External Enrichment. This bridges the gap between an anonymous ID and a usable persona.
6. Integrating the Technique into a Maltego Workflow
Step‑by‑step guide explaining what this does and how to use it.
For visual investigation, you can automate this entire process within Maltego. This involves creating custom entities and transforms. First, install the necessary Python libraries within your Maltego transform environment.
In your Maltego transform project directory pip install maltego-trx pyrogram
Create a new `Telegram StickerPack` entity in Maltego’s Entity Manager. Write a transform that takes a `Telegram Channel` entity, extracts posted sticker pack links, and applies the `>> 32` operation. The transform should output a new `Telegram User` entity with the derived User ID as a property. This allows you to click on any channel and visually discover potential creator IDs through its stickers, linking networks graphically.
7. Mitigations and Ethical Considerations
Step‑by‑step guide explaining what this does and how to use it.
If you are a security-conscious Telegram user, understanding this flaw is key to improving your operational security (OpSec). The primary mitigation is to never create or register official channel sticker packs using a personal or administrating account. Use a dedicated, burner account with no ties to your real identity or other operational channels. Furthermore, audit your channels and remove any custom sticker packs that were created before this practice was implemented. For investigators, always operate within legal boundaries; use these tools strictly for authorized threat intelligence, penetration testing on scoped assets, or academic research, ensuring compliance with laws like GDPR and CFAA.
What Undercode Say:
- Key Takeaway 1: Telegram’s sticker pack system contains a fundamental, non-cryptographic design flaw. The mapping of a pack ID to a creator’s User ID via a deterministic bit-shift operation exposes a severe attribution weakness, bypassing extensive privacy settings.
- Key Takeaway 2: The evolution from manual, academic exploitation (as detailed in the HackerNoon article) to an automated, weaponized CLI tool like TGSpyder represents a significant shift. It lowers the barrier to entry for sophisticated OSINT, allowing this advanced technique to be integrated into scalable investigative and threat-hunting pipelines.
Analysis:
The technique reveals a critical blind spot in secure platform design: privacy is often undermined not by broken encryption, but by metadata leakage in ancillary features. TGSpyder’s release formalizes this exploit, transforming it from a niche finding into a reusable asset. For defenders and intelligence units, this provides a powerful method to attribute otherwise anonymous criminal or extremist channels. For the threat actors themselves, it serves as a stark lesson in operational security, where even culturally embedded behaviors like using branded stickers must be scrutinized. The tool’s proxy support also highlights the dual-use nature of such software, enabling both ethical investigations and more advanced adversary tradecraft.
Prediction:
In the short term, we will observe a surge in the use of this technique by both ethical OSINT communities and malicious actors looking to dox competitors. Telegram may be forced to alter its sticker pack ID generation algorithm, sparking a cat-and-mouse game. In the longer term, this incident will accelerate the development of AI-driven tools that automatically correlate such leaked metadata across platforms, making persistent anonymity online exponentially more difficult. Consequently, advanced threat actors will adapt by developing and sharing strict OpSec protocols for brand management, potentially moving towards decentralized or blockchain-based messaging platforms that offer stricter control over all forms of metadata.
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IT/Security Reporter URL:
Reported By: Valdemarballe %F0%9D%97%9C%F0%9D%97%BB%F0%9D%98%81%F0%9D%97%BF%F0%9D%97%BC%F0%9D%97%B1%F0%9D%98%82%F0%9D%97%B0%F0%9D%97%B6%F0%9D%97%BB%F0%9D%97%B4 – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
