auto-attendance

v1.0.0 suspicious
6.0
Medium Risk

Professional real-time face recognition attendance system with anti-spoofing detection

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to network interactions, potential shell exploitation, and obfuscation techniques. While there's no clear indication of malicious intent, the combination of these factors raises concerns about its integrity and purpose.

  • network interaction with SMTP server
  • use of subprocess calls
  • code obfuscation via base64 decoding
Per-check LLM notes
  • Network: Email interaction with an SMTP server suggests potential data exfiltration or unauthorized communication.
  • Shell: Subprocess calls to execute scripts and upgrade pip may indicate benign package maintenance activities, but could also be used for nefarious purposes.
  • Obfuscation: The use of base64 decoding and dynamic imports suggests an attempt to obfuscate code execution, potentially hiding functionality.
  • Credentials: No direct evidence of credential harvesting is observed.
  • Metadata: The package shows signs of being new and possibly inactive, with links to non-secure sites and an author with minimal presence.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://autoattendance.readthedocs.io
  • Detailed PyPI description (190411 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
  • 27 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 9 commits in Mahfujul-01726/AutoAttendance
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • d email server = smtplib.SMTP(SMTP_SERVER, SMTP_PORT) server.starttls()
  • server = smtplib.SMTP(SMTP_SERVER, SMTP_PORT) server.starttls()
⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • nparr = np.frombuffer(base64.b64decode(encoded_data), np.uint8) frame = cv2.imdecode(nparr
  • ): try: __import__(import_name) print_success(f"{package_name}") exce
  • t FACE_DATA_DIR recognizer = __import__("face_recognition").FaceRecognitionModule() recognizer.load_model() print("\n"
  • s: try: __import__(package.replace('_', '-')) except ImportError: missing.append(p
⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • try: result = subprocess.run([sys.executable, "setup.py"], capture_output=False)
  • , '.join(missing)}") subprocess.run([sys.executable, '-m', 'pip', 'install', '--upgrade', 'pip']
  • '--upgrade', 'pip']) subprocess.run([sys.executable, '-m', 'pip', 'install'] + missing)
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: autoattendance.dev>

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:8000
  • Non-HTTPS external link: http://dlib.net/
⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 auto-attendance 
Create a fully-functional mini-app called 'SmartOfficeAttendance' using the Python package 'auto-attendance'. This app will serve as a real-time facial recognition attendance system for office environments, ensuring accurate employee check-ins while preventing spoofing attacks. The application should include the following key features:

1. **User Management**: Allow administrators to add, edit, and delete employee records. Each record should include a unique identifier, name, department, and associated facial images for training the recognition model.
2. **Real-Time Attendance Tracking**: Implement a live feed from a camera connected to the system, where the app continuously captures images of employees entering the office. Use the 'auto-attendance' package to perform real-time facial recognition on these images.
3. **Anti-Spoofing Detection**: Ensure that the system can differentiate between real faces and potential spoofing attempts such as photographs or masks. Utilize the anti-spoofing capabilities provided by the 'auto-attendance' package to enhance security.
4. **Attendance Logs**: Maintain a detailed log of all attendance events including date, time, user ID, and whether the entry was successful or flagged as suspicious due to potential spoofing.
5. **Alert System**: Configure notifications for administrators if there are any failed logins or suspicious activities detected by the anti-spoofing mechanism.
6. **Dashboard Interface**: Develop a simple web-based dashboard where administrators can view attendance logs, manage users, and monitor the status of the system in real-time.
7. **Data Privacy Compliance**: Ensure that all facial data captured and stored complies with relevant privacy laws and regulations, providing clear information about data usage and storage practices.

To utilize the 'auto-attendance' package effectively, you will need to integrate its core functionalities into each feature listed above, particularly focusing on facial recognition accuracy and anti-spoofing techniques. Additionally, consider adding optional features such as integration with existing HR systems or mobile apps for remote access to attendance data.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!