🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a notable shell risk due to detected shell executions, which could potentially be exploited for unauthorized command execution. However, other risks such as network, obfuscation, and credential risks are low.

High shell risk
No network calls
No obfuscation or credential harvesting

Per-check LLM notes

Network: No network calls were detected, which is neutral.
Shell: Detected shell execution suggests potential for unauthorized system commands, indicating higher risk.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

elf) -> None: code = "os.system(cmd)" matches = match_content(code) assert l
code = ( 'os.system(cmd)\n' 'api_key = "skliveabc123def456ghi"\n'
: self._process = subprocess.Popen( self._server_command, stdin

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: mythologiq.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ 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 4.0

2 maintainer concern(s) found

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 agent-failsafe

Create a mini-application called 'GovernanceGuard' that serves as a fail-safe monitor for a set of AI agents managed through the Microsoft Agent Governance Toolkit. This application will ensure that these agents adhere to strict operational guidelines and policies, automatically taking corrective actions when necessary. Utilize the 'agent-failsafe' package to implement its core functionality. Here’s a detailed breakdown of the project:

1. **Project Setup**: Initialize your Python environment and install the 'agent-failsafe' package alongside any other necessary dependencies such as requests, json, etc.
2. **Agent Configuration**: Define a configuration file where you specify the details of each AI agent including their unique identifiers, operational limits (e.g., data usage limits, response time thresholds), and the types of actions they can take (e.g., data retrieval, analysis).
3. **Monitoring Mechanism**: Implement a monitoring system within 'GovernanceGuard' that continuously checks each agent against their specified operational limits using the 'agent-failsafe' package. This includes real-time performance metrics and compliance checks.
4. **Fail-Safe Actions**: If an agent breaches its operational limits, 'GovernanceGuard' should automatically trigger predefined fail-safe actions. These could include pausing the agent, logging the breach, sending alerts to administrators, or even adjusting the agent’s parameters to prevent further breaches.
5. **User Interface**: Develop a simple command-line interface (CLI) that allows users to view the status of all agents, manually trigger a compliance check, and review logs of any breaches and actions taken.
6. **Testing & Validation**: Ensure that 'GovernanceGuard' works as expected by testing it with simulated agents and scenarios that mimic real-world conditions. Validate that the fail-safes are triggered correctly and that the application responds appropriately to different types of breaches.
7. **Documentation & Deployment**: Provide comprehensive documentation on how to set up 'GovernanceGuard', configure agents, and interpret its outputs. Consider deploying the application in a cloud environment for broader accessibility.

By completing this project, you will have a robust tool for ensuring the safe and compliant operation of AI agents, leveraging the advanced capabilities provided by the 'agent-failsafe' package.