🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks, particularly concerning shell execution and obfuscation techniques, which may indicate attempts to bypass security measures. The lack of proper metadata and the presence of non-HTTPS links further add to the suspicion.

Moderate shell execution risk
Potential obfuscation techniques
Lack of maintainer information and non-HTTPS links

Per-check LLM notes

Network: The network calls are likely for legitimate purposes such as making HTTP requests to external servers.
Shell: The shell execution patterns could be for package management tasks but may also indicate potential risk if not properly controlled, especially with the use of 'nosec' comments which might mask security issues.
Obfuscation: The code shows potential for obfuscation through environment variable checks and import statements, but without more context, it's hard to determine if this is malicious.
Credentials: No clear patterns of credential harvesting detected in the provided code snippet.
Metadata: The package contains suspicious non-HTTPS links and lacks a maintainer's GitHub repository or history.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

http_client=httpx.AsyncClient( verify=ssl_verify, timeout=DEFA
server.http_client = httpx.AsyncClient( verify=ssl_verify, timeout=DEFA
server.http_client = httpx.AsyncClient( verify=ssl_verify, timeout=DEFAULT_
http_client=httpx.AsyncClient(timeout=60), )
verify: http_client = httpx.Client(verify=False, timeout=timeout) # nosec B501 if provide

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

h data source. """ if __import__("os").getenv("ENABLE_KG_REGISTRY_FETCH", "true").lower() in (
cwd=cwd, env=({**__import__("os").environ, **proc_env} if proc_env else None), )

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

try: proc = subprocess.run( # nosec B603 B607 ["pip", "list", "--outda
try: proc = subprocess.run( # nosec B603 B607 ["pip-audit", "--format=
try: proc = subprocess.run( # nosec B603 B607 ["pip", "list", "--forma
import subprocess subprocess.check_call( [ sys.executable,
l_session_name, cmd] subprocess.run(launch_cmd, check=True) return { "statu
try: proc = subprocess.run( # noqa: S603 - bin resolved via shutil.which, args are sta

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

⚠ Suspicious Page Links score 8.0

Found 4 suspicious link(s) on the package page

Non-HTTPS external link: http://langfuse.arpa/api/public/otel
Non-HTTPS external link: http://langfuse.arpa
Non-HTTPS external link: http://vllm.arpa/v1
Non-HTTPS external link: http://vllm-embed.arpa/v1

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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-utilities

Create a fully functional mini-application called 'AgentTaskMaster' using the Python package 'agent-utilities'. This application will serve as a task management system specifically tailored for AI agents, enabling them to manage their tasks efficiently and effectively. The application should include the following features:

1. **User Interface**: Develop a simple, intuitive user interface that allows users to interact with the application easily.
2. **Task Creation**: Users should be able to create new tasks for AI agents, specifying details such as task description, priority level, due date, and associated tags.
3. **Task Management**: Implement functionalities to manage these tasks, including marking tasks as completed, editing task details, and setting reminders for upcoming deadlines.
4. **Task Prioritization**: Integrate a feature that allows users to prioritize tasks based on urgency and importance, helping AI agents focus on high-priority tasks first.
5. **Tagging System**: Enable users to categorize tasks using tags, making it easier to filter and search for specific tasks.
6. **Integration with 'agent-utilities'**: Utilize the 'agent-utilities' package to enhance the functionality of your application. Specifically, leverage its utilities for handling Pydantic models, data validation, and AI agent communication to streamline task creation and management processes.
7. **Data Persistence**: Ensure that all task data is stored persistently, possibly using a database like SQLite or PostgreSQL, so that the application state is preserved across sessions.
8. **Testing**: Write comprehensive tests to ensure the application functions correctly and robustly handles various scenarios.

Your goal is to demonstrate how the 'agent-utilities' package can be effectively integrated into a real-world application, showcasing its capabilities in simplifying complex task management operations for AI agents.