AI Analysis
The package shows moderate risk due to its obfuscation practices and limited maintainer history, raising suspicion about its true intentions and development legitimacy.
- Moderate obfuscation risk
- Minimal maintainer history
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
- Obfuscation: The observed patterns suggest some level of obfuscation, possibly to hide code logic, but not necessarily malicious.
- Credentials: No clear evidence of credential harvesting detected.
- Metadata: The package has minimal maintainer history and an underdeveloped git repository, raising concerns about its legitimacy.
Heuristic Checks
No suspicious network call patterns found
Found 4 obfuscation pattern(s)
try: decoded = base64.b64decode(m.group(1)).decode("utf-8") return json.loads(de4 += "=" * padding return base64.b64decode(b64).hex() # Copyright 2024-2026 Tymofii Pidlisnyi. Apache-") try: sig_hex = base64.b64decode(sig_b64).hex() except Exception: return {"ok": Ft) try: sig_hex = base64.b64decode(attest["signature_b64"]).hex() except Exception:
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: aeoess.com>
All external links appear legitimate
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a mini-application named 'IdentityGuard' that leverages the 'agent-passport-system' Python package to manage digital identities and their attributions within a secure network environment. This application will serve as a sandboxed playground to understand and experiment with the core functionalities of the 'agent-passport-system', such as identity management, data source registration, and mutual authentication. Step 1: Setup - Initialize a new Python virtual environment and install the 'agent-passport-system' package. - Set up a basic Flask or Django web framework to host the application. Step 2: User Registration and Identity Management - Implement a user registration system where users can create accounts with unique digital identities. - Use the 'agent-passport-system' package to generate and manage these identities securely. Step 3: Data Source Registration - Allow users to register different data sources associated with their identities. - Utilize the package's capabilities to ensure the integrity and authenticity of these data sources. Step 4: Mutual Authentication - Develop a feature where users can authenticate each other based on their registered identities and data sources. - Use the 'agent-passport-system' to facilitate this process securely. Step 5: Training Attribution and Periodic Settlements - Simulate a scenario where users can train models using data from registered data sources. - Implement a system to attribute the training process to specific identities and settle periodic fees or credits accordingly. Suggested Features: - Integration with OAuth 2.0 for external identity providers. - A dashboard for users to view and manage their identities and data sources. - Real-time notifications for successful or failed authentications and settlements. - Detailed logs and audit trails for all actions performed within the application. This project aims to provide a comprehensive understanding of the 'agent-passport-system' package's capabilities while building a functional and secure mini-application.