🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of obfuscation and potential interaction with system files, indicating possible malicious intent. However, there is no clear evidence of direct credential harvesting or active malicious behavior.

High obfuscation risk
Potential interaction with system files

Per-check LLM notes

Obfuscation: The code pattern suggests an attempt to dynamically import modules, which is often used for obfuscation to hide the actual functionality.
Credentials: The detected pattern does not clearly indicate direct credential harvesting but may imply interaction with system files, raising suspicion about potential misuse.
Metadata: The author's name is missing and they appear to be inactive, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

try: with urllib.request.urlopen(url, timeout=1) as r: if r.status ==
urllib.request with urllib.request.urlopen(f"{agentguard_server.base_url}/health") as r:
se audit query. req = urllib.request.Request( f"{agentguard_server.base_url}/v1/audit
ey}"}, ) with urllib.request.urlopen(req) as r: entries = json.loads(r.read()
HTTPError) as ei: urllib.request.urlopen(f"{agentguard_server.base_url}/v1/audit") as
urllib.request with urllib.request.urlopen(f"{agentguard_server.base_url}/dashboard") as r:

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

try: module = __import__(module_name, fromlist=[attr]) base = getattr(module, attr, None)
try: module = __import__(module_name, fromlist=[attr]) base = getattr(module, attr, None) exce

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

ommand(cmd: str): os.system(cmd) run_command("ls") # executes if ALLOW
nv = env self._proc = subprocess.Popen( command, stdin=subprocess.PIPE,
os.environ.copy() proc = subprocess.Popen( [ BINARY, "serve",

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

file", "arguments": {"path": "/etc/hosts"}}, }) # Upstream got the call. ass

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Caua-ferraz/AgentGuard 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 agentguardproxy

Your task is to develop a simple yet effective command-line tool using Python, which leverages the 'agentguardproxy' package to create a secure environment for managing AI agents. This tool will serve as a firewall for AI agents, ensuring they operate within predefined safe boundaries. Here’s a detailed breakdown of the project requirements and steps to get you started:

1. **Project Setup**: Begin by installing the necessary packages including 'agentguardproxy'. Ensure your development environment is set up properly.
2. **Core Functionality**:
- Implement a command-line interface (CLI) that allows users to add, remove, and manage AI agents.
- Utilize 'agentguardproxy' to monitor and control access points for these agents, ensuring they only perform actions that have been explicitly allowed.
3. **Features to Include**:
- **Agent Management**: Users should be able to add new agents, modify existing ones, and delete them when no longer needed.
- **Access Control**: Define rules using 'agentguardproxy' to restrict the types of operations each agent can perform. For example, an agent might be allowed to read data but not write it.
- **Audit Log**: Maintain a log of all actions performed by the agents, including any attempts to violate the defined access controls.
4. **Implementation Steps**:
- Step 1: Create a class or module to represent an AI agent, incorporating methods to define its capabilities and restrictions.
- Step 2: Integrate 'agentguardproxy' into your project to handle the enforcement of these restrictions.
- Step 3: Develop the CLI using a library like argparse to allow users to interact with the system.
- Step 4: Test your implementation thoroughly, simulating various scenarios to ensure the security measures work as intended.
5. **Enhancements (Optional)**:
- Consider adding support for real-time monitoring and alerts for suspicious activities.
- Implement a user authentication system to further secure the management of AI agents.
6. **Documentation**: Provide clear documentation on how to install and use the tool, including examples of how to configure different levels of access for AI agents.

By completing this project, you will gain valuable experience in securing AI applications and utilizing advanced Python packages such as 'agentguardproxy'. Good luck!