amd-gaia

v0.20.0 suspicious
8.0
High Risk

GAIA is a lightweight agent framework designed for the edge and AI PCs.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high credential risk and moderate obfuscation risk, suggesting potential malicious activities. However, the low metadata risk provides some reassurance.

  • High credential risk due to harvesting patterns
  • Moderate obfuscation risk using ROT13 encoding
Per-check LLM notes
  • Obfuscation: The use of ROT13 encoding is often employed for simple obfuscation but can also be used maliciously to hide code or data.
  • Credentials: Patterns indicating attempts to harvest AWS and Kubernetes configuration files suggest potential malicious intent for stealing secrets.
  • Metadata: The author 'AMD' has only one package on PyPI, which may indicate a new or less active account.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (7142 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
  • 488 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 7 unique contributor(s) across 100 commits in amd/gaia
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • 38 | response = requests.get(url, timeout=60) >>> 39 | models = response.json()
  • y: resp = httpx.get(url, timeout=15, follow_redirects=True)
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • decoded = codecs.decode(name, "rot_13") except Exception:
⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • : subprocess.run(["open", str(path)], check=False) el
  • : subprocess.run(["xdg-open", str(path)], check=False) except
  • try: output = subprocess.check_output( ["cmdkey", "/list"], text=T
  • try: output = subprocess.check_output( [ "powershell",
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • ration", True), (".aws/credentials", "AWS credentials", True), (".kube/config", "K
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository amd/gaia appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AMD" 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 amd-gaia 
Develop a real-time environmental monitoring system using the 'amd-gaia' package. This system will leverage the lightweight agent framework provided by GAIA to manage multiple sensors deployed across various locations. The application should be capable of collecting data from different types of sensors (e.g., temperature, humidity, air quality) and sending this information to a central server for analysis and visualization.

Step 1: Set up your development environment. Ensure you have Python installed and create a virtual environment. Install the 'amd-gaia' package along with any necessary dependencies for interacting with sensors.

Step 2: Design the architecture of your system. Define agents for each sensor location that will collect data periodically. Each agent should be able to communicate with the central server to send collected data.

Step 3: Implement the agent framework using 'amd-gaia'. Customize the agents to interact with specific sensor APIs or hardware interfaces. Configure the agents to run in a loop, collecting data at regular intervals and sending it to the central server.

Step 4: Develop the central server component. This part of the application should receive data from all agents and store it in a database. Additionally, implement basic analytics to provide insights into the environmental conditions.

Step 5: Create a web-based dashboard to visualize the collected data. Users should be able to see real-time updates on environmental conditions from different locations. Include options for filtering data based on time periods and sensor types.

Suggested Features:
- Support for adding/removing sensors dynamically without restarting the system.
- Ability to configure different types of sensors through a simple configuration file.
- Historical data storage and retrieval for trend analysis.
- Alerts for abnormal environmental conditions (e.g., high temperature, low air quality).

How 'amd-gaia' is Utilized:
- Use 'amd-gaia' to define and manage agents for each sensor location. Leverage its lightweight design for efficient operation on edge devices.
- Take advantage of 'amd-gaia's communication capabilities to ensure reliable data transmission from agents to the central server.
- Employ 'amd-gaia's flexibility to customize agents according to the specific requirements of different sensors.