AI Analysis
The package shows moderate risk due to potential credential harvesting and obfuscation practices that could mask malicious activities. While not conclusive evidence of malice, these factors warrant caution.
- High credential risk
- Potential obfuscation for hiding malicious code
Per-check LLM notes
- Network: The package makes network calls to IPFS and Vercel storage, which may be legitimate for data storage and retrieval but could also indicate unexpected behavior depending on the package's intended functionality.
- Shell: No shell execution patterns were detected.
- Obfuscation: Base64 decoding without validation can introduce security risks, but it may also be used for legitimate purposes like data decryption.
- Credentials: Multiple getenv calls for AWS credentials suggest the package might harvest environment variables, which could be a security risk if not handled properly.
- Metadata: The maintainer has only one package and the git repository is not found, which could indicate suspicious activity.
Package Quality Overall: Medium (6.2/10)
Test suite present — 6 test file(s) found
Test runner config found: pyproject.toml6 test file(s) detected (e.g. test_aws.py)
Some documentation present
Documentation URL: "Documentation" -> https://github.com/altcodepro/polydb-python#readmeDetailed PyPI description (7671 chars)
Has contribution guidelines and governance files
Governance file: security.pyDevelopment Status classifier >= Beta
Partial type annotation coverage
518 type-annotated function signatures detected in source
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
Found 6 network call pattern(s)
port requests resp = requests.post(f"{self.ipfs_url}/api/v0/add", files={"file": data}, timeoutport requests resp = requests.post(f"{self.ipfs_url}/api/v0/cat", params={"arg": cid}, timeout=] = v response = requests.put( f"https://blob.vercel-storage.com/{blob_keytes() response = requests.get( f"https://blob.vercel-storage.com/{key}",True response = requests.delete( f"https://blob.vercel-storage.com/{key}",sults response = requests.get( f"https://blob.vercel-storage.com/?prefix={
Found 4 obfuscation pattern(s)
y: return base64.b64decode(payload, validate=True) except TypeError:return base64.b64decode(payload) except Exception:f key_str: return base64.b64decode(key_str) # Generate new key (should be saved secureprefix combined = base64.b64decode(encrypted_data) nonce = combined[:12]
No shell execution patterns detected
Found 5 credential access pattern(s)
ion = ( region or os.getenv("AWS_REGION") or os.getenv("AWS_DEFAULT_REGION") or "us-east-1"or os.getenv("AWS_REGION") or os.getenv("AWS_DEFAULT_REGION") or "us-east-1" ) # LocalStendpoint_url or os.getenv("AWS_ENDPOINT_URL") or os.getenv("LOCALSTACK_ENDPOINTor os.getenv("AWS_REGION") or os.getenv("AWS_DEFAULT_REGION") or "us-east-1" ) self.endpndpoint_url = endpoint_url or os.getenv("AWS_ENDPOINT_URL") self._client: Any = None sel
No typosquatting candidates detected
No author email provided
All external links appear legitimate
Repository not found (deleted or private)
Repository not found (deleted or private)
1 maintainer concern(s) found
Author "AltCodePro" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a simple inventory management system using the 'altcodepro-polydb-python' package. This system will allow users to add, update, delete, and view items in their inventory. Additionally, it should support searching for items based on various criteria such as name, category, or quantity. The application should be designed to run both locally and deployed in a cloud environment, showcasing the multi-cloud capabilities of the 'altcodepro-polydb-python' package. It should utilize the connection pooling, retry logic, and thread safety features provided by the package to ensure efficient and reliable data operations. Steps to create the application: 1. Set up a Python virtual environment and install 'altcodepro-polydb-python'. 2. Define the structure of your inventory database, including tables for items, categories, and suppliers. 3. Implement functions to connect to the database, ensuring you use the connection pooling feature for efficiency. 4. Create CRUD (Create, Read, Update, Delete) operations for managing inventory items. 5. Add search functionality allowing users to filter items by name, category, or supplier. 6. Utilize the retry logic provided by the package to handle transient errors during database operations. 7. Ensure thread safety when multiple users are accessing the inventory simultaneously. 8. Test the application thoroughly in different environments to confirm its reliability and performance. 9. Optionally, wrap the application in a simple web interface using Flask or Django, allowing users to interact with the inventory through a browser.
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