azol

v0.5.3 suspicious
6.0
Medium Risk

A python-based pentesting library for Azure and Entra ID

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of legitimate functionality but raises concerns due to high risks associated with network and credential handling, as well as potential obfuscation practices.

  • High network risk
  • High credential risk
  • Potential obfuscation to hide functionality
Per-check LLM notes
  • Network: The observed network calls resemble authentication and possibly data factory interactions, which could be legitimate but also indicative of unauthorized access attempts.
  • Shell: No shell execution patterns detected.
  • Obfuscation: The use of base64 decoding and AES encryption is common in legitimate applications for data protection, but the lack of context suggests potential obfuscation to hide functionality.
  • Credentials: The use of getpass.getpass indicates handling of sensitive credentials, which could be legitimate but also suggests risk if not properly secured, as it may imply direct user interaction for inputting secrets.
  • Metadata: The author's new account and lack of repository indicate potential low effort or inactive status, raising some suspicion.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1251 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • } response = requests.post(self.dataFactory_url + "/subscriptions/00000000-0000-0000-00
  • 00000/token" response=requests.post(request_url, headers=headers, params=query_params, json=body
  • } response = requests.post(f"{self.dataFactory_url}/tenants/{self.subscription_id}/gate
  • } response = requests.post(f"{self.dataFactory_url}/tenants/{self.subscription_id}/gate
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • ry decoded_df_token = base64.b64decode(bearer_token) df_token_dict = json.loads(decoded_df_
  • encrypted_bytes_aes_key=base64.b64decode(encrypted_b64_aes_key) aes_key=self._agent_
  • json["iv"] iv=base64.b64decode(iv_b64) encrypted_b64_message=message_json["
  • encrypted_bytes=base64.b64decode(encrypted_b64_message) cipher = Cipher(self
  • d = int.from_bytes(base64.b64decode(b64_d), "big") q = int.from_bytes(base64.b64deco
  • q = int.from_bytes(base64.b64decode(b64_q), "big") p = int.from_bytes(base64.b64deco
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 5.0

Found 2 credential access pattern(s)

  • sword: password = getpass.getpass('Password:') data={ "login": username,
  • d: password = getpass.getpass('Password:') data={ "login": us
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Cody Burkard" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with azol 
Create a security assessment tool named 'AzureSentry' using the Python package 'azol'. This tool will be designed to help cybersecurity professionals perform basic penetration testing on Azure and Entra ID environments. The goal of 'AzureSentry' is to automate the process of identifying potential vulnerabilities in these cloud services, making it easier for teams to assess their security posture regularly.

### Key Features:
1. **Login and Authentication**: Integrate with Azure Active Directory to authenticate users and gain access to the Azure environment.
2. **Resource Scanning**: Automatically scan all resources within a specified Azure subscription to identify potential security issues such as misconfigured permissions, exposed resources, etc.
3. **Vulnerability Reporting**: Generate detailed reports that outline the findings from the scans, including recommendations for remediation.
4. **Customizable Scan Settings**: Allow users to customize the depth and scope of the scans based on their specific needs.
5. **Integration with Other Tools**: Provide an API or command-line interface for integrating 'AzureSentry' with other security tools or platforms.

### Utilizing the 'azol' Package:
- Use 'azol' to handle authentication with Azure and Entra ID, ensuring secure and efficient access to the necessary APIs.
- Leverage 'azol' functions to interact with Azure resources, such as listing subscriptions, resource groups, and individual resources.
- Employ 'azol' for vulnerability scanning by utilizing its built-in checks and heuristics to evaluate resource configurations against known best practices and common security pitfalls.
- Rely on 'azol' to generate comprehensive reports based on the scan results, which include actionable insights and recommendations for improving security.

### Development Steps:
1. Set up a Python environment with the necessary dependencies, including the 'azol' package.
2. Implement the login and authentication functionality, allowing users to securely connect to their Azure environment.
3. Develop the resource scanning module, focusing on identifying misconfigurations and other potential security risks.
4. Create a reporting system that summarizes the findings and provides guidance on how to address any detected issues.
5. Add customizable settings to allow users to tailor the scanning process to their requirements.
6. Design an API or CLI for easy integration with other tools and platforms.
7. Test 'AzureSentry' thoroughly to ensure it works as expected across different scenarios and environments.
8. Document the project, including setup instructions, usage examples, and API documentation.
9. Release 'AzureSentry' as an open-source project, encouraging contributions and feedback from the community.

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