🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential risk, particularly due to shell execution and the inability to locate its repository. However, there is no clear evidence of malicious intent.

Shell risk (5/10) due to possible execution of shell commands
Repository not found, raising concerns about the package's origin and maintenance

Per-check LLM notes

Network: The network calls appear to be API interactions which could be legitimate if the SDK is designed to interact with a service.
Shell: Executing shell commands can be risky as it may indicate the package is performing actions that could be exploited, such as dependency management or running server-side scripts.
Obfuscation: No obfuscation patterns detected in the provided snippet.
Credentials: The use of 'keyring.get_password' suggests secure storage retrieval, but without context, it could potentially be used for credential harvesting.
Metadata: The repository not being found and the maintainer having only one package suggest potential risks, but no clear malicious intent is evident.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

l yourself response = requests.get( token_info["api_call"]["full_url"],
ts ... response = requests.get( ... token_info.full_url, ...
lf >>> response = requests.get( ... token_info.full_url, ...
try: response = httpx.get( f"{config.gateway_url}/api/v1/applications"
r try: response = httpx.delete( f"{config.gateway_url}/api/v1/applications/{app
try: response = httpx.post( f"{gateway_url}/api/v1/auth/resolve-org",

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

her Unix result = subprocess.run( ["ps", "-p", str(ppid), "-o", "comm="],
else: result = subprocess.run( ["ps", "-p", str(ppid), "-o", "args="],
ver dependencies...") subprocess.run(["npm", "install"], cwd=str(vm_dir), check=True) # Pass
...") try: proc = subprocess.run( ["node", str(server_js)], env=env,

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

token_json = keyring.get_password( self.SERVICE_NAME,

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: agentic-fabriq.org

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Agentic Fabriq Contributors" 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 agentic-fabriq-sdk

Create a mini-application named 'AI Agent Manager' that allows users to manage and interact with multiple AI agents through a simple command-line interface (CLI). This application will utilize the 'agentic-fabriq-sdk' package to handle authentication, deployment, and interaction with AI agents. Here’s a step-by-step guide on what the application should do and how it should be structured:

1. **Authentication**: Implement user authentication using the SDK's authentication capabilities. Users should be able to log in using their credentials and receive an access token for subsequent requests.
2. **Agent Management**: Allow users to create, delete, and list AI agents they have access to. Each agent should have a unique identifier, name, and description.
3. **Deployment**: Provide functionality to deploy new AI agents. Users should be able to specify the type of agent (e.g., language model, image generator) and any necessary configurations.
4. **Interaction**: Enable users to send prompts to AI agents and receive responses. Ensure that the CLI provides a clean and intuitive way for users to communicate with their agents.
5. **Configuration**: Include options for users to configure settings related to each agent, such as API rate limits, logging preferences, etc.
6. **Help and Documentation**: Offer comprehensive help documentation within the CLI, including examples of how to use different commands and manage agents effectively.

**Features to Consider**:
- Support for multiple environments (development, staging, production).
- Ability to switch between different accounts or organizations.
- Detailed logging of interactions and operations performed via the CLI.
- Integration with cloud storage services for backing up agent configurations.
- Command history and autocomplete for ease of use.

By leveraging the 'agentic-fabriq-sdk', you'll be able to streamline the process of managing AI agents, making it easier for developers and users alike to work with these powerful tools.