algorand-python-testing

v1.1.0 suspicious
5.0
Medium Risk

Algorand Python testing library

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high obfuscation risk through base64 encoding, which could indicate malicious intent. However, other risks like shell execution and missing maintainer metadata are less severe, leading to an overall moderate risk level.

  • High obfuscation risk due to base64 encoding
  • Missing maintainer metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network functionality.
  • Shell: Shell execution is observed but seems related to compiling or generating code, potentially part of the package's intended functionality.
  • Obfuscation: The code shows signs of using base64 encoding for obfuscation purposes which could be indicative of malicious activity.
  • Credentials: No clear evidence of credential harvesting is present in the provided code snippets.
  • Metadata: The maintainer's author name is missing and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (7.4/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • Test runner config found: pyproject.toml
  • 7 test file(s) detected (e.g. test_contract.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://github.com/algorandfoundation/puya/tree/main/algopy_
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3486 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 583 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 8 unique contributor(s) across 100 commits in algorandfoundation/puya
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • e == Base64.URLEncoding else base64.b64decode(a_str) ) return Bytes(result) from __future__ impor
  • Y=")`""" return Bytes(base64.b64decode(value)) @staticmethod def from_hex(value: str) -> B
⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • lidate-abi-return", ] subprocess.run( compile_cmd, check=True, env=ENV_WI
  • ", out_dir, ] subprocess.run( compile_cmd, check=True, env=ENV_WI
  • path), ] try: subprocess.run( generate_cmd, check=True,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: algorand.foundation>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository algorandfoundation/puya appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with algorand-python-testing 
Create a mini-application called 'AlgoTestRunner' using the 'algorand-python-testing' package. This application will serve as a tool for developers to easily test their Algorand smart contracts and transactions before deploying them on the mainnet.

The app should have the following core functionalities:
1. **Setup and Configuration**: Allow users to configure the app with their Algorand account details such as private key and address. It should also support switching between sandbox mode and live network testing.
2. **Smart Contract Testing**: Provide a feature to upload and compile Algorand smart contracts. The app should then allow users to define test cases for these contracts, including input parameters and expected outcomes. Users should be able to run these tests and receive detailed reports on the success or failure of each test case.
3. **Transaction Simulation**: Enable users to simulate sending various types of transactions (e.g., payment, asset creation, asset transfer) without actually broadcasting them to the network. This feature should generate a summary report showing the state of the accounts after each simulated transaction.
4. **Error Handling and Logging**: Implement robust error handling and logging mechanisms to capture any issues during contract compilation or transaction simulation. Logs should include timestamps, error messages, and steps taken when errors occur.
5. **User Interface**: Develop a simple but intuitive command-line interface (CLI) for interacting with the app. Additionally, consider adding a basic web-based UI for more interactive testing sessions.

To utilize the 'algorand-python-testing' package, you'll need to leverage its capabilities for setting up test environments, compiling smart contracts, and simulating transactions. Specifically, use it to handle the low-level interactions with the Algorand network, ensuring that all operations are performed securely and efficiently.

Your goal is to create a comprehensive tool that simplifies the process of testing Algorand applications, making it easier for developers to catch bugs and ensure their code works as expected before going live.

💬 Discussion Feed

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