appworld-sdk

v0.1.7 suspicious
6.0
Medium Risk

Client SDK for App World — RL & Eval environments for Android apps

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to potential obfuscation techniques and the use of local eval, which can be exploited for malicious purposes. While there's no concrete evidence of malicious activity, the package's metadata and repository details raise concerns.

  • High obfuscation risk
  • Use of local eval
Per-check LLM notes
  • Network: The observed network calls are typical for an SDK that interacts with an API server.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The code pattern suggests an attempt to decode and process an image from a base64 encoded string, which could be part of normal functionality but also indicates potential for hiding malicious code.
  • Credentials: No clear evidence of direct credential harvesting is present, but the presence of local eval suggests caution as it can be used to execute arbitrary code.
  • Metadata: The package has no typosquatting or email domain flags, but the repository is not found and the maintainer has few contributions, raising suspicion.

📦 Package Quality Overall: Low (3.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/anthropics/app-world#readme
  • Detailed PyPI description (3593 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 55 type-annotated function signatures detected in source
○ 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)

  • e) -> Any: response = requests.get( f"{self.base_url}{path}", headers=s
  • ]) -> Any: response = requests.post( f"{self.base_url}{path}", headers=s
  • r) -> Any: response = requests.delete( f"{self.base_url}{path}", headers=s
  • str, Any]: response = requests.post( f"{self.base_url}{path}", headers=s
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • rl.split(",", 1) binary = base64.b64decode(payload) return np.array(Image.open(BytesIO(binary)).con
  • ----------------- # Local eval (LLM runs on user side, env interactions via API) # -----
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ 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 2.0

1 maintainer concern(s) found

  • Author "App World Contributors" 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 appworld-sdk 
Create a Python-based mini-application that leverages the 'appworld-sdk' package to automate the testing and evaluation of Android applications using Reinforcement Learning (RL) techniques. This tool will serve as a bridge between developers and automated testing frameworks, allowing for more efficient and thorough testing of Android apps.

Step-by-Step Instructions:
1. Set up your development environment with Python and install the 'appworld-sdk' package.
2. Define the scope of the application, such as which types of Android apps you want to test (e.g., games, productivity tools).
3. Utilize the 'appworld-sdk' package to set up the RL environment for the chosen type of Android app.
4. Implement an RL algorithm to interact with the Android app through the RL environment provided by 'appworld-sdk'.
5. Develop a feedback mechanism that evaluates the performance of the app based on user interactions and RL outcomes.
6. Integrate logging and reporting functionalities to track the progress and results of the tests.
7. Enhance the application by adding features such as customizable RL parameters, support for multiple Android versions, and the ability to run tests on different devices or emulators.
8. Ensure the application is user-friendly and can be easily integrated into existing development workflows.

Suggested Features:
- Support for various RL algorithms (e.g., DQN, PPO)
- Customizable test scenarios and goals
- Detailed reporting and analytics of test results
- Integration with popular CI/CD pipelines
- User interface for managing and monitoring tests

Utilization of 'appworld-sdk':
The 'appworld-sdk' package will be central to setting up and managing the RL environment for Android apps. It will handle the communication between the RL algorithm and the Android app, providing a seamless way to simulate user interactions and evaluate the app's response. By leveraging the capabilities of 'appworld-sdk', the application will be able to conduct comprehensive tests that mimic real-world usage scenarios, thereby enhancing the quality and reliability of the tested Android apps.

💬 Discussion Feed

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