aws_lambda_stream

v1.4.0 suspicious
6.0
Medium Risk

Create stream processors with AWS Lambda functions

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential obfuscation and improper credential handling, raising concerns about its integrity. While the network and metadata risks are relatively low, the elevated obfuscation and credential risks warrant further investigation.

  • High obfuscation risk due to base64 decoding
  • Concerns over credential handling due to incomplete and obfuscated code
Per-check LLM notes
  • Network: The detected network patterns suggest legitimate interactions with an Elasticsearch-like API, possibly for indexing and mapping purposes.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: Base64 decoding of data suggests data might be encoded for transmission, but unusual context indicates potential for hiding malicious content.
  • Credentials: Environment variable access is standard practice, but the incomplete and potentially obfuscated code raises concerns about improper handling or misuse of credentials.
  • Metadata: The maintainer has only one package, which could indicate a new or less active account, but there are no other red flags.

📦 Package Quality Overall: Medium (5.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (19105 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

  • 58 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 52 commits in clandro89/aws-lambda-stream
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • index, options): r = requests.put( "{}/{}".format(self.host, index), a
  • dex(self, index): r = requests.get( "{}/{}".format(self.host, index), a
  • , index, params): r = requests.put( "{}/{}/_mapping".format(self.host, index),
  • dex(self, index): r = requests.delete( "{}/{}".format(self.host, index), a
  • ex(self, params): r = requests.post( "{}/_reindex".format(self.host), au
  • , alias, params): r = requests.put( "{}/{}/_alias/{}".format(self.host, index, alia
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • record, 'event': base64.b64decode(record["kinesis"]["data"]) } ).map( lamb
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 7.5

Found 3 credential access pattern(s)

  • { 'functionname': os.getenv('AWS_LAMBDA_FUNCTION_NAME'), 'pipeline': err.uow.get(
  • ndefined'), 'region': os.getenv('AWS_REGION', 'undefined'), 'stage': os.getenv('STAGE', '
  • ed'), 'functionname': os.getenv('AWS_LAMBDA_FUNCTION_NAME', 'undefined'), 'pipeline': pip
✓ 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 clandro89/aws-lambda-stream appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Alejandro Hernández" 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_lambda_stream 
Create a real-time data processing application using AWS Lambda and the 'aws_lambda_stream' package. This application will process incoming data streams from various sources such as IoT devices, social media feeds, or web server logs. Your task is to develop a mini-application that demonstrates the power of AWS Lambda in handling real-time data processing efficiently.

### Application Overview:
- **Name**: Real-Time Data Processor
- **Purpose**: To ingest, process, and analyze streaming data in real-time from multiple sources.
- **Technologies Used**: AWS Lambda, AWS Kinesis Streams, Python, and the 'aws_lambda_stream' package.

### Core Features:
1. **Data Ingestion**: The application should be able to receive data from different sources. For simplicity, you can simulate this by sending test data through AWS Kinesis Streams.
2. **Real-Time Processing**: Implement real-time data processing logic using AWS Lambda functions. Use the 'aws_lambda_stream' package to handle the stream processing efficiently.
3. **Data Analysis**: Perform basic analysis on the processed data, such as calculating average values, identifying trends, or detecting anomalies.
4. **Visualization**: Display the analyzed data in a simple, readable format, either through a console output or a basic web dashboard.

### Step-by-Step Implementation Guide:
1. **Setup AWS Environment**:
   - Create an AWS account if you don't have one.
   - Set up an AWS Kinesis Stream to serve as the data source.
2. **Install Dependencies**:
   - Install Python and necessary libraries including 'boto3', 'aws_lambda_stream'.
3. **Define Stream Processor Function**:
   - Use 'aws_lambda_stream' to define a function that processes each record from the Kinesis Stream.
4. **Deploy Lambda Function**:
   - Deploy your stream processor function as an AWS Lambda function.
5. **Integrate with Kinesis Stream**:
   - Configure the Kinesis Stream to trigger your Lambda function whenever new data arrives.
6. **Test the Application**:
   - Simulate data ingestion into the Kinesis Stream.
   - Observe the processed and analyzed data output.
7. **Enhance with Visualization**:
   - Implement a simple visualization component to display the processed data.

### Suggested Enhancements:
- Add error handling and retries for failed data processing.
- Implement more sophisticated analysis techniques like machine learning models for predictive analytics.
- Expand the application to support multiple types of data sources.
- Integrate with other AWS services like DynamoDB for storing processed data.

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