agent_governance_toolkit

v4.0.0 suspicious
6.0
Medium Risk

Public Preview — Unified installer and runtime policy enforcement for the Agent Governance Toolkit

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows several concerning signs including high shell risk, credential risk, and obfuscation techniques. However, there is no conclusive evidence of malicious intent.

  • High shell risk due to use of os.system() and subprocess.run()
  • Potential credential harvesting through access to environment variables
Per-check LLM notes
  • Network: The network calls may be legitimate if the package requires API interactions for governance actions.
  • Shell: Use of os.system() and subprocess.run() can introduce risks if not properly sanitized, especially when executing external commands.
  • Obfuscation: Base64 decoding and lowercase transformation of the README content may indicate an attempt to hide or standardize text, but could also be for formatting purposes.
  • Credentials: The code snippet accessing environment variables for tokens suggests potential unauthorized harvesting of credentials unless explicitly documented as necessary for the package's functionality.
  • Metadata: The maintainer has a new or inactive account and lacks a full author name, which raises some suspicion but not enough to conclude malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • }/json" req = urllib.request.Request(url, headers={"Accept": "application/json"}) # noqa
  • endpoint with urllib.request.urlopen(req, timeout=10) as resp: # noqa: S310 — URL from c
  • ckage}" req = urllib.request.Request(url, headers={"Accept": "application/json"}) # noqa
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • readme_text = base64.b64decode(readme["content"]).decode("utf-8", errors="replace").lower()
  • r"\beval\s*\("), "eval() detected", "Avoid eval() - use ast.literal_eva
  • detected", "Avoid eval() - use ast.literal_eval or safer alternatives", ),
  • (re.compile(r"\beval\s*\("), "eval() detected", "Use JSON.parse or safer alternatives"),
  • kstemp", "B307": "eval() is dangerous - find alternative approach", "B3
  • -pattern", title="eval() usage", file="demo.py", line=10,
⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • \.system\s*\("), "os.system() detected", "Use subprocess.run() with argument
  • try: result = subprocess.run( ["gh", "auth", "token"], ca
  • ) detected", "Use subprocess.run() with argument list", ), ] _JS_DANGEROUS_PA
  • try: result = subprocess.run( # noqa: S603 — resolved absolute path [det
  • )]) result = subprocess.run( # noqa: S603 — trusted subprocess in security scanner
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • return _TOKEN token = os.environ.get("GITHUB_TOKEN") or os.environ.get("GH_TOKEN") if not token: t
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: microsoft.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository microsoft/agent-governance-toolkit appears legitimate

⚠ 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_governance_toolkit 
Create a fully-functional mini-application named 'AgentPolicyEnforcer' using the Python package 'agent_governance_toolkit'. This application will serve as a simple yet powerful tool for managing and enforcing runtime policies on agents within a network environment. The goal of this application is to demonstrate the capabilities of 'agent_governance_toolkit' by implementing several core functionalities such as installing agents, configuring policies, and monitoring compliance status.

### Features:
1. **Agent Installation**: Users should be able to install agents using the toolkit. This involves specifying the type of agent, version, and any specific configurations needed.
2. **Policy Configuration**: Allow users to define and configure runtime policies for these agents. Policies could include restrictions on data access, communication protocols, and operational limits.
3. **Monitoring Compliance**: Implement a feature that continuously monitors all installed agents to ensure they comply with the defined policies. Provide real-time alerts if any agent violates the policies.
4. **Reporting**: Generate comprehensive reports on agent compliance and policy violations. Reports should include details like timestamp of violation, type of violation, and affected agents.
5. **User Interface**: Develop a simple command-line interface (CLI) for ease of use. Additionally, consider integrating a basic web interface using Flask for more advanced users.

### Utilizing 'agent_governance_toolkit':
- Use the toolkit's unified installer functionality to streamline the process of adding new agents to the network.
- Leverage the runtime policy enforcement capabilities to dynamically adjust and enforce security policies across all managed agents.
- Integrate the monitoring and reporting modules provided by the toolkit to ensure continuous oversight and accountability.

### Steps to Build the Application:
1. **Setup Environment**: Install Python and necessary libraries including 'agent_governance_toolkit'.
2. **Design Architecture**: Plan out the structure of your application, considering both backend logic and frontend presentation layers.
3. **Implement Core Functions**: Focus on building out the agent installation, policy configuration, and monitoring functionalities.
4. **Develop UI/UX**: Create a user-friendly CLI and, optionally, a web interface using Flask.
5. **Testing & Validation**: Thoroughly test each feature to ensure reliability and accuracy.
6. **Documentation**: Write clear documentation explaining how to install, configure, and operate 'AgentPolicyEnforcer'.
7. **Deployment**: Prepare the application for deployment in a production environment.

This project not only showcases the power of 'agent_governance_toolkit' but also provides a practical solution for network administrators looking to enhance their security posture.