🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its high network and shell execution risks, despite having low obfuscation and credential harvesting risks. The metadata also lacks critical information.

High network risk due to external resource fetching
High shell risk due to subprocess execution

Per-check LLM notes

Network: The network requests appear to be fetching external resources, which could potentially be used for unauthorized data exchange.
Shell: Executing commands via subprocess suggests the package may interact with the system at a low level, increasing the risk of unintended behavior or security vulnerabilities.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some red flags, such as lack of maintainer information and classifiers, but there are no clear signs of malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

kage}/json" req = urllib.request.Request(url, headers={"User-Agent": "ErisPulse-Dashboard/1.0
board/1.0"}) with urllib.request.urlopen(req, timeout=15) as resp: data = jso
nc_fetch(): req = urllib.request.Request(url, headers={"User-Agent": "ErisPulse-Dashboard/1.0
board/1.0"}) with urllib.request.urlopen(req, timeout=10) as resp: return jso
try: req = urllib.request.Request(url, headers={"User-Agent": "ErisPulse-Dashboard/1.0
d/1.0"}) with urllib.request.urlopen(req, timeout=10) as resp: data =

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

try: proc = subprocess.Popen( cmd, stdout=subprocess.PIPE
kage_name] proc = subprocess.run(cmd, capture_output=True, text=True, timeout=120)
(packages) proc = subprocess.Popen( pip_cmd, stdout=subprocess.

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ErisPulse-Dashboard

Develop a real-time health monitoring dashboard using the ErisPulse-Dashboard package. This dashboard will serve as a tool for individuals or healthcare professionals to monitor various health metrics in real-time, providing insights into overall health status. The application should include the following key features:

1. User Authentication: Implement a simple user registration and login system to ensure data privacy and security.
2. Data Collection: Integrate with wearable devices or health trackers to collect real-time health data such as heart rate, blood pressure, and sleep patterns.
3. Data Visualization: Utilize the ErisPulse-Dashboard package to create interactive charts and graphs that display the collected health metrics over time. Include features like zooming, panning, and tooltips for better data exploration.
4. Alerts & Notifications: Set up customizable alert systems based on predefined thresholds for health metrics. For example, if a user's heart rate exceeds a certain level, send them a notification via email or SMS.
5. Historical Data Analysis: Allow users to view historical data trends and compare their current health status against past records.
6. Customizable Dashboards: Enable users to personalize their dashboards by adding/removing widgets and adjusting layouts according to their preferences.
7. Integration with External APIs: Connect the dashboard to external health APIs to fetch additional health-related information, such as weather conditions or air quality index, which could impact health.
8. Mobile Responsiveness: Ensure the dashboard is accessible and functional on both desktop and mobile devices.

Utilize the ErisPulse-Dashboard package primarily for its advanced data visualization capabilities, enabling you to present complex health data in an intuitive and engaging manner. Additionally, explore other functionalities provided by the package that can enhance the user experience and make the dashboard more robust.