amazon-bedrock-haystack

v6.11.0 suspicious
6.0
Medium Risk

An integration of AWS S3 and Bedrock as a Downloader and Generator components.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential credential exposure and some level of obfuscation, suggesting it may have underlying issues that need further investigation.

  • Credential risk 7/10 due to environment variable access for AWS credentials
  • Obfuscation risk 5/10 due to base64 decoding of file contents
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
  • Obfuscation: Base64 decoding of file contents may indicate an attempt to conceal code or data, but could also be legitimate for various purposes such as data transmission.
  • Credentials: Environment variable access for AWS credentials suggests potential exposure to unauthorized access risks, especially if not properly secured or validated.
  • Metadata: The author information is sparse and the maintainer has a single package, which could indicate a less experienced or potentially suspicious user.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 13 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/deepset-ai/haystack-core-integrations/tre
  • Detailed PyPI description (934 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

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

Active multi-contributor project

  • 16 unique contributor(s) across 100 commits in deepset-ai/haystack-core-integrations
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • "source": {"bytes": base64.b64decode(file_content.base64_data)}, "name": "samplep
  • "source": {"bytes": base64.b64decode(file_content.base64_data)}, "name": "filenam
  • "source": {"bytes": base64.b64decode(file_content.base64_data)}, } } def
  • "source": {"bytes": base64.b64decode(base64_image)}, }
  • "source": {"bytes": base64.b64decode(base64_image)}, } },
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 10.0

Found 6 credential access pattern(s)

  • variables aws_profile_name = os.environ.get("AWS_PROFILE") or "default" aws_region_name = os.environ.get("AWS
  • r "default" aws_region_name = os.environ.get("AWS_DEFAULT_REGION") or "eu-central-1" # Initialize the AmazonBe
  • r None endpoint_url = os.getenv("AWS_ENDPOINT_URL") or None return cls( s3_bu
  • test.mark.skipif( not os.getenv("AWS_BEDROCK_GUARDRAIL_ID") or not os.getenv("AWS_BEDROCK_GUARDRA
  • BEDROCK_GUARDRAIL_ID") or not os.getenv("AWS_BEDROCK_GUARDRAIL_VERSION"), reason=( "E
  • "guardrailIdentifier": os.getenv("AWS_BEDROCK_GUARDRAIL_ID"), "guardrailVersion":
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: deepset.ai>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository deepset-ai/haystack-core-integrations 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 amazon-bedrock-haystack 
Create a document management system that integrates AWS S3 and Bedrock services using the 'amazon-bedrock-haystack' Python package. This system will enable users to upload documents to an S3 bucket, generate summaries and key points from those documents using Bedrock's capabilities, and provide a searchable database of these summaries and key points. The application should have the following functionalities:

1. User Authentication: Implement user authentication to ensure only authorized users can access the system.
2. Document Upload: Allow users to upload various types of documents (PDFs, Word documents, etc.) to the S3 bucket.
3. Document Processing: Utilize 'amazon-bedrock-haystack' to process uploaded documents, generating summaries and extracting key points.
4. Searchable Database: Create a searchable database where users can search for documents based on their summaries and extracted key points.
5. Document Viewing: Provide functionality for users to view the original documents stored in the S3 bucket.
6. Summary Display: Display the generated summaries and key points alongside the original documents.
7. Notifications: Send email notifications to users when their document processing is complete.

The 'amazon-bedrock-haystack' package will be used to handle the downloading of documents from S3 and generating summaries and key points using Bedrock's natural language processing capabilities. Users should be able to interact with the system through a web interface built using Flask or Django, and all interactions with AWS services should be managed securely.