🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks associated with network and metadata analysis, suggesting potential issues that require further investigation.

network risk due to external communication
metadata indicating low effort and potential anonymity

Per-check LLM notes

Network: The network call pattern suggests the package may be communicating with an external service, which is common but requires scrutiny to ensure it's not misused.
Shell: No shell execution patterns were detected, indicating low risk of direct system command execution.
Obfuscation: The observed patterns suggest legitimate use of base64 decoding for cryptographic operations rather than obfuscation.
Credentials: No suspicious patterns indicating credential harvesting were detected.
Metadata: The package shows signs of low effort and potential anonymity, which could indicate suspicious activity.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

meout self._client = httpx.Client(timeout=self.timeout) def close(self): self.

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

() private_key_bytes = base64.b64decode(private_key_b64) if HAS_NACL: # nacl format:
n}".encode() signature = base64.b64decode(headers["x-signature"]) pub = base64.b64decode(pub_b64)
rs["x-signature"]) pub = base64.b64decode(pub_b64) try: import nacl.signing vk

✓ 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

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
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 agentlock-sdk

Create a Python-based desktop application named 'AgentGuardian' that integrates with the AgentLock SDK to manage and secure AI agents within a user's environment. This application will serve as a central hub for deploying, monitoring, and controlling AI agents, ensuring they operate within defined security parameters and guidelines.

Key Features:
1. **Agent Management**: Users can deploy new AI agents from a pre-approved list of templates, each with unique capabilities and purposes. Each agent deployment should be securely initialized using the AgentLock SDK.
2. **Security Policies**: Implement customizable security policies that define how AI agents can interact with system resources (e.g., network access, file operations). These policies should be enforced by the AgentLock SDK during runtime.
3. **Monitoring & Logging**: Real-time monitoring of AI agent activities and performance metrics. Logs should include any violations of security policies and be stored securely.
4. **User Interface**: Develop a user-friendly graphical interface that allows users to easily manage their AI agents and review logs. The UI should clearly display any security warnings or alerts.
5. **Customization**: Allow advanced users to customize their AI agents further by integrating additional modules or modifying existing ones, all while maintaining security through the AgentLock SDK.

How to Use the AgentLock SDK:
- Utilize the AgentLock SDK to initialize and manage AI agents, ensuring they adhere to specified security policies from deployment onwards.
- Leverage the SDK's logging capabilities to monitor agent behavior and enforce compliance with security protocols.
- Integrate the SDK into your application's workflow to automate policy enforcement and alert generation based on real-time data.