🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories with no signs of malicious activity or supply-chain attack indicators.

Low network, shell, obfuscation, and credential risks
Metadata quality is low but does not indicate malicious intent

Per-check LLM notes

Network: Network calls appear to be standard API interactions, likely for communication with a service named 'plato'.
Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: Low risk, but requires attention due to incomplete author information and low metadata quality.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

try: resp = requests.post(f"{self.plato_url}/room/{self.room}", json=tile, timeout=5)
try: resp = requests.get(f"{self.plato_url}/room/{self.room}?limit=20", timeout=5)

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ 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

Repository SuperInstance/activeledger-agent appears legitimate

⚠ 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 activeledger-agent

Create a Python-based mini-application called 'Plato Fleet Manager' that leverages the 'activeledger-agent' package to manage and monitor a fleet of devices within the PLATO ecosystem. This application will serve as a centralized management tool for fleet administrators to perform various operations such as device registration, status monitoring, firmware updates, and security checks.

Step 1: Set up the environment
- Install Python and necessary libraries including 'activeledger-agent'.

Step 2: Define the Application Structure
- Create a main module to handle user inputs and interactions.
- Develop a 'DeviceManager' class that utilizes 'activeledger-agent' to interact with the PLATO network.

Step 3: Implement Core Features
- Device Registration: Allow users to register new devices with unique identifiers.
- Status Monitoring: Fetch and display real-time status updates from devices.
- Firmware Updates: Push firmware updates to devices based on their current version.
- Security Checks: Perform regular security audits on devices using 'activeledger-agent'.

Step 4: Enhance User Experience
- Integrate command-line interface (CLI) for easy interaction.
- Implement logging for tracking actions performed on devices.

Utilize the 'activeledger-agent' package to establish connections with the PLATO network, authenticate requests, and execute commands on devices. Ensure that all operations comply with the PLATO protocol standards.