aiobotocore

v3.7.0 safe
3.0
Low Risk

Async client for aws services using botocore and aiohttp

🤖 AI Analysis

Final verdict: SAFE

The package aiobotocore v3.7.0 presents minimal risks based on the analysis, with no clear indicators of malicious behavior. It uses common practices for network and subprocess handling.

  • Standard use of AsyncClient and ClientSession
  • Environment variable checks for AWS regions
Per-check LLM notes
  • Network: The use of AsyncClient and ClientSession is standard for making asynchronous network calls in Python, suggesting legitimate functionality.
  • Shell: Use of subprocess.run indicates the package can execute external commands, which could be risky if not properly sanitized or controlled.
  • Obfuscation: Base64 decoding is commonly used for data encoding and not necessarily indicative of malicious activity.
  • Credentials: Environment variable checks for AWS regions are standard practices for retrieving configuration settings and do not indicate credential harvesting.
  • Metadata: The package shows some minor red flags such as an author with a missing name and possibly a new account, but no clear signs of malicious intent or typosquatting.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 24 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://aiobotocore.aio-libs.org
  • Detailed PyPI description (26797 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 26 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 100 commits in aio-libs/aiobotocore
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • None self._session = httpx.AsyncClient( timeout=self._timeout, limits=limits, cert=cert
  • sync_context( aiohttp.ClientSession( connector=connector,
⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • if self._checksum.digest() != base64.b64decode(self._expected): error_msg = ( f
  • ] if checksum.digest() != base64.b64decode(expected): error_msg = ( f"Expected chec
  • sha256_trailer_checksum = base64.b64decode(resp['ChecksumSHA256']) assert digest == sha256_trailer
  • ream.read() log_result = base64.b64decode(invoke_response["LogResult"]) assert json.loads(data) =
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • `asyncio.to_thread``. p = subprocess.run(process_list, capture_output=True, check=False) return p
  • s): # We're not using shell=True, so we need to pass the # command and all arguments
⚠ Credential Harvesting score 7.5

Found 3 credential access pattern(s)

  • rent_region = None if os.environ.get('AWS_EXECUTION_ENV'): default_region = os.environ.get
  • default_region = os.environ.get('AWS_DEFAULT_REGION') current_region = os.environ.get
  • current_region = os.environ.get('AWS_REGION', default_region) if not current_region:
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://mypy-lang.org/
✓ Git Repository History

Repository aio-libs/aiobotocore 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 aiobotocore 
Create a fully functional asynchronous AWS S3 file management utility using Python's 'aiobotocore' package. This utility will enable users to perform common S3 operations such as uploading files, listing all objects in a bucket, downloading files, and deleting files from their AWS S3 storage. Additionally, implement a feature to asynchronously copy files between buckets or within the same bucket, and another feature to asynchronously delete multiple files at once. The application should also include error handling for common issues like invalid credentials, non-existent buckets, and file permissions errors. Use 'aiobotocore' to ensure all operations are performed asynchronously for efficiency. The final product should be a command-line interface (CLI) tool where users can input commands to manage their S3 files.

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