Skip to main content

Validates the existence of registered accounts across social & shopping platforms and extracts rich identity intelligence (Names, Photos, IDs).

Project description

Aarya (आर्य)

The Advanced OSINT Email Scanner

PyPI version License: GPL v3

Aarya is an OSINT tool that validates the existence of email addresses across social media, shopping, and professional platforms (e.g. Instagram, Amazon, Spotify).

  • It leverages Asynchronous HTTP Requests (httpx) to perform lightning-fast, concurrent checks without the overhead of a web browser.
  • It silently verifies accounts using "Forgot Password" APIs, registration endpoints, and public profile scrapes.

Aarya Demo

🚀 Features

  • Deep Analysis: Goes beyond simple "Yes/No" results to extract rich metadata like Google Maps reviews, Profile Pictures, Gaia IDs, and ProtonMail key creation dates.
  • Full Visibility: Reports positive hits, negative results, rate limits, and errors explicitly so you never miss a detail.
  • Smart Stealth: Automatically fetches the latest real-world User-Agents from the web to bypass simple bot detection filters.
  • Elegant UI: Professional, minimalist CLI design with responsive tables and clean link wrapping.

🆚 Aarya vs. Holehe

During development of this tool I came to know that another great tool was already there which was similar to Aarya.

here is why Aarya outperforms.

Feature Holehe Aarya
Primary Output Email Existence (True/False) Identity Intelligence (Real Names, Photos, Maps Reviews)
Reliability Prone to False Negatives High (Explicitly detects Rate Limits vs. Not Found)
Stealth Static Headers Dynamic (Auto-fetches latest User-Agents)
Focus Quantity (120+ Sites) Quality (Deep scans of High-Value Targets)
UI/UX Basic CLI Modern (Rich Tables, Clickable Links, Summary Panels)

🔍 Use Cases in Recon & Intel

1. Verification & Validation

Confirm if a target email is active. A "ghost" email (no accounts anywhere) is a high-risk indicator for fraud or burner accounts, whereas an email with established accounts verifies the identity exists.

2. Social Engineering Context

Aarya helps Red Teamers map the digital footprint of a target. Knowing a target uses Duolingo or Wattpad allows for highly tailored phishing pretexts (e.g., "Your Duolingo streak is in danger" vs generic corporate emails).

3. Identity Correlation

By extracting unique identifiers like the Google Gaia ID or ProtonMail public key date, Aarya helps correlate an email address with real-world timelines, locations, and other digital identities across the web.

4. Credibility of Credential Reuse (Post-Exploitation)

If a target's password is compromised (via phishing or a data breach) for one verified platform, Aarya provides a precise roadmap of other active services where that same password might be reused, highlighting critical risks for credential stuffing attacks.

5. Corporate OpSec Auditing

Security teams can scan corporate email domains to detect "Shadow IT" or policy violations. Discovering that an employee used their official name@company.com address to sign up for Instagram or Amazon highlights potential attack surfaces and credential leakage risks.

6. OSINT Pivot Points

Aarya acts as a signpost for deeper investigation. A confirmed Google account signals an investigator to search for public Maps reviews or Photos. A confirmed Instagram account invites a search for public profile associated with that email. The tool identifies where to look next for public data.

7. Credibility Analysis (Anti-Fraud)

In fraud investigations, account age acts as a trust signal. An email address linked to a ProtonMail key created 3 years ago or a Google account with Maps contributions from 2019 is far more likely to be legitimate than a "fresh" email with absolutely no digital footprint.

📦 Installation

pip install aarya

🛠 Usage

Basic Scan:

aarya target@example.com

Save Results:

aarya target@example.com -o results.json

🧩 Supported Platforms

Aarya currently performs deep scans on the following high-value services:

  • Social: Instagram, Twitter (X), Wattpad, About.me
  • Shopping: Amazon, Flipkart
  • Music & Learning: Spotify, Duolingo
  • Mail: Gmail (Advanced), ProtonMail
  • More platforms to be added soon...

⚠️ Disclaimer

Aarya is designed for educational purposes, authorized security research, and personal digital footprint analysis only.

The developers are not responsible for any misuse of this tool. Scanning email addresses that do not belong to you or without the owner's explicit consent may violate privacy laws or platform Terms of Service in your jurisdiction. Use responsibly.

🤝 Contributing

Contributions are welcome! If you want to add a new module (e.g., Pinterest, Adobe), please fork the repository and submit a Pull Request.

📜 License

This project is licensed under the GNU General Public License v3.0. See the LICENSE file for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

aarya-1.0.0.tar.gz (54.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

aarya-1.0.0-py3-none-any.whl (43.6 kB view details)

Uploaded Python 3

File details

Details for the file aarya-1.0.0.tar.gz.

File metadata

  • Download URL: aarya-1.0.0.tar.gz
  • Upload date:
  • Size: 54.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.7

File hashes

Hashes for aarya-1.0.0.tar.gz
Algorithm Hash digest
SHA256 13114232e477f5a0ac9d5e00adeb888159f16e0cd7470283bdc834a792d3e475
MD5 a81bc97e00fd1375e90e8df206176895
BLAKE2b-256 f9d1f6e9e79a4ae3f9563948dec118b02c7c7d7335f424fd2deff2d71d966567

See more details on using hashes here.

File details

Details for the file aarya-1.0.0-py3-none-any.whl.

File metadata

  • Download URL: aarya-1.0.0-py3-none-any.whl
  • Upload date:
  • Size: 43.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.7

File hashes

Hashes for aarya-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 4bf450edd561ad8501f38d965104245c01a0d5949597bf757b883463dab77d5e
MD5 08448a2432e310a121cc2b1244d4620a
BLAKE2b-256 1439ea6dd867975c7fdba745e673a62cd8b4042c5ad76b204ad4e9a0881c2f40

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page