async-kinesis

v2.5.6 suspicious
5.0
Medium Risk

AsyncIO Kinesis Library

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some potentially risky behaviors, particularly around credential handling and obfuscation, which while not conclusive evidence of malice, warrant caution and further scrutiny.

  • credential risk due to potential misconfiguration
  • obfuscation that may obscure code logic
Per-check LLM notes
  • Network: The network calls appear to be testing local connections and are not indicative of malicious behavior.
  • Shell: The shell executions are related to running tests and code formatters which are common practices in software development but should still be scrutinized for correctness.
  • Obfuscation: The obfuscation pattern is not typical of malicious activity but may indicate an attempt to obscure code logic, which could be suspicious.
  • Credentials: The use of environment variables for AWS credentials is common practice but the incomplete snippet suggests potential misconfiguration or exposure risks.
  • Metadata: The maintainer has only one other package, suggesting a new or less active account which may warrant further investigation.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 22 test file(s) found

  • Test runner config found: conftest.py
  • 22 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (31168 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

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

Active multi-contributor project

  • 8 unique contributor(s) across 100 commits in hampsterx/async-kinesis
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • return False sock = socket.create_connection((parsed.hostname, parsed.port), timeout=1) sock.clos
  • mport urllib.request urllib.request.urlopen("http://localhost:4566/", timeout=2) print("
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • t", lambda _: __import__("kinesis.timeout_compat", fromlist=["timeout"]).timeout(0.5), ), ): with py
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • nning tests...") result = subprocess.run(["python", "-m", "pytest", "-x"], capture_output=True, text=
  • .") # Black result = subprocess.run(["black", "--check", "kinesis", "tests"], capture_output=Tru
  • lse # isort result = subprocess.run(["isort", "--check-only", "kinesis", "tests"], capture_outpu
  • se # flake8 result = subprocess.run( [ "flake8", "kinesis",
  • rectory is clean result = subprocess.run(["git", "status", "--porcelain"], capture_output=True, text=
  • n False # Create tag subprocess.run(["git", "tag", version], check=True) print(f"Created tag
⚠ Credential Harvesting score 5.0

Found 2 credential access pattern(s)

  • .region_name = region_name or os.environ.get("AWS_DEFAULT_REGION", "us-east-1") self.ttl_hours = ttl_h
  • _argument("--region", default=os.environ.get("AWS_DEFAULT_REGION", "us-east-1")) parser.add_argument("--st
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository hampsterx/async-kinesis appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "hampsterx" 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 async-kinesis 
Create a real-time data processing mini-application using the 'async-kinesis' Python package. This application will serve as a simple stream processor that ingests data from an Amazon Kinesis Data Stream, processes it asynchronously, and then outputs the processed data to another Kinesis stream or a different data sink like an S3 bucket or a database. The goal is to demonstrate the power of asynchronous programming in handling large volumes of streaming data efficiently.

### Project Overview:
- **Name**: AsyncKinesisStreamProcessor
- **Objective**: Build a lightweight but powerful stream processor using 'async-kinesis'.
- **Features**:
  - Asynchronous data ingestion from a specified Kinesis stream.
  - Real-time data processing with custom logic (e.g., filtering, transformation).
  - Output the processed data to another Kinesis stream or a file.
  - Support for multiple consumers to handle high throughput streams.
  - Graceful shutdown to ensure no data loss during termination.

### Steps to Implement:
1. **Setup Environment**:
   - Install 'async-kinesis' and other necessary packages.
2. **Configuration**:
   - Define the source and destination Kinesis streams.
   - Set up AWS credentials and region.
3. **Data Ingestion**:
   - Use 'async-kinesis' to asynchronously read records from the source stream.
4. **Processing Logic**:
   - Implement the processing logic based on the business requirements (e.g., filter out certain types of records, transform data).
5. **Output Handling**:
   - Decide on the output method (another Kinesis stream, S3, or a database).
   - Write the processed data to the chosen destination.
6. **Graceful Shutdown**:
   - Ensure the application can shut down gracefully without losing any data in transit.
7. **Testing**:
   - Test the application with sample data to ensure correctness and performance.
8. **Documentation**:
   - Provide clear documentation on how to run the application, including setup instructions and configuration details.

### Utilizing 'async-kinesis':
- **Asynchronous Reading**: Use 'async-kinesis' to set up an asynchronous reader that continuously fetches data from the source Kinesis stream.
- **Error Handling**: Implement robust error handling to manage potential issues such as network failures or data corruption.
- **Concurrency Control**: Leverage the asynchronous nature of 'async-kinesis' to handle multiple concurrent tasks efficiently.
- **Logging**: Incorporate logging to track the progress and troubleshoot issues.

This project aims to showcase the capabilities of 'async-kinesis' in building scalable and efficient real-time data processing applications.

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