🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits behaviors that suggest potential misuse, including network calls to an OpenAI API and execution of external scripts, indicating a high risk of unauthorized actions.

unexpected network calls
execution of external scripts

Per-check LLM notes

Network: The package makes unexpected calls to an OpenAI API endpoint, which may indicate unauthorized use or data exfiltration.
Shell: The package executes external scripts using subprocess, which can be used to run arbitrary code and poses a significant risk if not properly sanitized.
Obfuscation: The use of base64 decoding suggests some level of obfuscation which could be used to hide code or data, but it is not definitively malicious.
Credentials: No patterns indicative of credential harvesting were found.
Metadata: Package shows signs of low activity and effort, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

3 test file(s) detected (e.g. test_auth.py)

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

46 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

"requests.post", "urllib.request.urlopen" } def validate_tool_call(self, tool_na
oint") try: res = requests.post("https://api.openai.com/v1/chat/completions", json={"prompt"
I Endpoint") try: requests.post("https://api.openai.com/v1/chat/completions", json={"prompt"

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

decoded_bytes = base64.b64decode(padded_word.encode('utf-8'), validate=True)

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

stration...") subprocess.check_call([sys.executable, injector_path]) except Exception as

✓ 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

Only one version has ever been released — brand new package
Author "Antigravity" 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 agentshield-firewall

Create a mini-application called 'AI Security Monitor' that leverages the 'agentshield-firewall' package to provide real-time security monitoring and protection for AI agents. This application should be designed to run on a local network and monitor incoming and outgoing traffic from AI agents to detect and block malicious activities.

Steps to follow:
1. Set up a basic Flask web server as the backend for your application. Ensure it has endpoints to start, stop, and configure the firewall based on user inputs.
2. Integrate 'agentshield-firewall' into your application to enable the firewall functionality. Use its API to define rules for blocking known threats and suspicious behaviors.
3. Implement a feature that allows users to view the current status of the firewall, including which rules are active and any recent blocked attempts.
4. Add a logging mechanism to record all security events and provide an endpoint for retrieving these logs.
5. Develop a simple frontend using HTML/CSS/JavaScript that connects to the Flask server to provide a user-friendly interface for managing the firewall.
6. Include an alert system that notifies users via email or SMS if critical security incidents are detected.
7. Test your application thoroughly to ensure it works as expected under various conditions.
8. Document your setup process and how to use the application for others to replicate or extend.

Suggested Features:
- Rule-based firewall configuration allowing users to add custom rules for specific IP addresses or domains.
- Integration with external threat intelligence feeds to automatically update the firewall rules.
- Real-time monitoring dashboard displaying current network activity and potential threats.
- Historical data analysis tools to help identify patterns or trends in security incidents.

The 'agentshield-firewall' package will be utilized primarily for defining and enforcing firewall rules, handling network traffic inspection, and providing a secure environment for AI agents. Your goal is to create a robust and user-friendly tool that enhances the security posture of AI-driven systems.