DangQu

v0.1.13 suspicious
5.0
Medium Risk

dangqu sdk

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to its network activity and obfuscation techniques, which could potentially be used for malicious purposes.

  • High network risk due to external API calls
  • Moderate obfuscation risk with base64 encoding
Per-check LLM notes
  • Network: The observed network calls suggest the package is designed to send files and base64 encoded strings to an external API, which could potentially be for legitimate purposes like OCR processing or image analysis, but may also indicate data exfiltration.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The use of base64 decoding and image handling suggests potential obfuscation but could also be legitimate for image processing tasks.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The maintainer has only one package and lacks a GitHub repository, which may indicate a new or less active account, but no other suspicious activities are flagged.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • f.read() resp = requests.post(api_url, files={"image": file}) return resp.json()
  • ocr/b64/json" resp = requests.post(api_url, data=base64_str) return resp.json()
  • det/b64/json" resp = requests.post(api_url, data=base64_str) return resp.json()
  • d}/file/json" resp = requests.post( api_url, files={"target_img": target_file_byte
  • od}/b64/json" resp = requests.post(api_url, data=base64.b64encode(json_str.encode()).decode())
⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • [1] image_data = base64.b64decode(image_data) image = Image.open(BytesIO(image_da
  • ",")[1] image_data = base64.b64decode(background) background_ = Image.open(BytesIO(image_
  • split(",")[1] img_data = base64.b64decode(base64_str) np_arr = np.frombuffer(img_data, np.uint8)
  • [1] image_data = base64.b64decode(image_data) image = Image.open(io.BytesIO(image
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: lumi.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "DMJ-11740" 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 DangQu 
Develop a weather forecast mini-app using the DangQu SDK, which is designed to provide weather data services. Your task is to create a user-friendly interface that fetches and displays real-time weather conditions for any given city. Additionally, incorporate a feature that predicts the weather for the next 5 days. The app should also allow users to save their favorite cities for quick access.

Step-by-Step Instructions:
1. Set up your development environment with Python and install the DangQu package.
2. Create a main menu where users can enter the name of a city to get its current weather details.
3. Use the DangQu SDK to fetch the current temperature, humidity, wind speed, and other relevant weather information.
4. Display the fetched weather details in a readable format on the screen.
5. Implement a feature to show the 5-day weather forecast for the selected city.
6. Add functionality to save favorite cities and display them in a list for easy access.
7. Ensure the app has error handling for invalid city names and API rate limits.
8. Test the app thoroughly with various cities to ensure reliability.
9. Document your code and include comments explaining the use of DangQu methods and functions.

Suggested Features:
- Interactive command-line interface for better user experience.
- Support for multiple languages to cater to a global audience.
- Integration with an external database to store user preferences.
- Notification system to alert users about severe weather conditions.

How to Utilize DangQu:
- Use DangQu's API endpoints to retrieve real-time and forecasted weather data.
- Handle authentication tokens provided by DangQu for accessing premium weather data services.
- Parse JSON responses from DangQu to extract specific weather attributes like temperature, humidity, etc.