aws-durable-execution-sdk-python

v1.5.0 safe
4.0
Medium Risk

AWS Durable Execution SDK for Python

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of risk with no network calls and low obfuscation. While there is a moderate risk associated with credential handling and metadata transparency, these do not strongly indicate malicious intent.

  • Moderate credential risk due to reliance on environment variables
  • Sparse metadata about the author
Per-check LLM notes
  • Network: No network calls detected, which is normal and not indicative of any risk.
  • Shell: The use of shell commands to check git status and log is unusual for a production runtime but might be part of development or testing processes. This could indicate benign use or potential misuse.
  • Obfuscation: No obfuscation patterns detected.
  • Credentials: The code is accessing environment variables for credentials, which is common practice but should ensure proper handling and secure storage of these variables.
  • Metadata: The author information is sparse, indicating a potential lack of transparency or a new/inactive maintainer.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • Test runner config found: pyproject.toml
  • 3 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/aws/aws-durable-execution-sdk-python#read
  • Detailed PyPI description (4135 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

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

Active multi-contributor project

  • 25 unique contributor(s) across 100 commits in aws/aws-durable-execution-sdk-python
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • dirty_check: status = subprocess.run( ["git", "status", "--porcelain"], c
  • , ) result = subprocess.run( ["git", "log", "--no-merges", git_range, "-z", "--f
⚠ Credential Harvesting score 10.0

Found 4 credential access pattern(s)

  • KMS key.""" account_id = os.getenv("AWS_ACCOUNT_ID") region = os.getenv("AWS_REGION", "us-west-2
  • AWS_ACCOUNT_ID") region = os.getenv("AWS_REGION", "us-west-2") if not account_id: return
  • config = { "region": os.getenv("AWS_REGION", "us-west-2"), "lambda_endpoint": os.getenv(
  • ), "account_id": os.getenv("AWS_ACCOUNT_ID"), "kms_key_arn": os.getenv("KMS_KEY_ARN"
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: amazon.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aws/aws-durable-execution-sdk-python 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 aws-durable-execution-sdk-python 
Create a mini-application called 'Durable Task Manager' using the AWS Durable Execution SDK for Python. This application will allow users to submit tasks to an AWS Step Functions workflow, monitor their status, and retrieve results in a user-friendly way. Here are the key steps and features you'll need to implement:

1. **Setup**: Begin by setting up your environment with the necessary AWS credentials and installing the 'aws-durable-execution-sdk-python' package.
2. **Task Submission**: Implement functionality to accept task definitions from the user, such as specifying input parameters for a task. These tasks will then be submitted to a predefined AWS Step Functions state machine via the SDK.
3. **Status Monitoring**: Develop a feature to periodically check the status of submitted tasks and display it to the user. Users should be able to see if a task is running, succeeded, failed, or still pending.
4. **Result Retrieval**: Once a task has completed successfully, provide an option for users to view the output or result of the task execution.
5. **Error Handling**: Ensure that any errors encountered during task submission or execution are handled gracefully and communicated clearly to the user.
6. **User Interface**: Design a simple command-line interface (CLI) or a basic web frontend for interacting with the application.
7. **Documentation**: Write comprehensive documentation detailing how to install and use the application, including examples of common tasks that can be executed through the Durable Task Manager.

The 'aws-durable-execution-sdk-python' package will be crucial for interacting with AWS Step Functions. Use its capabilities to handle long-running or complex workflows efficiently, ensuring that tasks can be retried automatically in case of failures and that they can be resumed after interruptions.

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