asta-agent

v1.0.15 suspicious
7.0
High Risk

High-level SDK for building A2A-compatible AI agent servers

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant risks due to its network and shell execution capabilities, which are not well-documented. Additionally, there's a high risk associated with credential handling.

  • High network risk
  • Executing external commands
  • Potential credential harvesting
Per-check LLM notes
  • Network: The package makes network calls that could potentially be used for sending data outside the system, which is suspicious without clear documentation.
  • Shell: Executing external commands can introduce risks if not properly sanitized, suggesting potential for unintended actions or security vulnerabilities.
  • Obfuscation: No obfuscation patterns detected in the provided code snippet.
  • Credentials: High risk of credential harvesting as the code is attempting to retrieve an API key from environment variables.
  • Metadata: The package shows signs of low maintainer effort and lack of transparency.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • Test runner config found: conftest.py
  • 5 test file(s) detected (e.g. conftest.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

  • 147 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 9.0

Found 6 network call pattern(s)

  • {self.api_key}" req = urllib.request.Request(url, headers=headers) try: resp
  • try: resp = urllib.request.urlopen(req, timeout=30) return json.loads(resp.
  • ollow for POST. req = urllib.request.Request( self.base_url + "/", data=data, headers
  • try: resp = urllib.request.urlopen(req, timeout=120) body = json.loads(resp
  • try: with urllib.request.urlopen(f"{url}/healthz", timeout=2) as r: i
  • self._http_client = httpx.AsyncClient(timeout=10) return self._http_client async def
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • delete=False ) proc = subprocess.Popen( [ PY, "-m", "uvicorn", "fake_theorizer:
  • nv.update(env_extra) cp = subprocess.run( [PY, str(driver), *args], env=env, capture_
  • ask still listed.""" cp = subprocess.run( [PY, str(driver), "theorizer", "--help"], e
  • agent): procs = [ subprocess.Popen( [PY, str(driver), "theorizer", "--refresh-card"
  • (driver, cache_dir): cp = subprocess.run( [PY, str(driver), "theorizer", "card"], env
⚠ Credential Harvesting score 5.0

Found 2 credential access pattern(s)

  • RL"), api_key=os.environ.get("API_KEY"), ) return self._app def build(
  • on.get("ASTA_A2A_API_KEY") or os.environ.get("API_KEY")): return env try: from asta.utils.aut
✓ 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

  • 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 asta-agent 
Create a mini-application named 'AIChatBot' using the 'asta-agent' package, which is designed for building A2A-compatible AI agent servers. This application will serve as a simple chatbot capable of interacting with users through text-based conversations. It will utilize the high-level functionalities provided by 'asta-agent' to streamline the setup and management of the AI agent server.

Step 1: Set up the environment
- Install Python and ensure you have pip installed.
- Use pip to install the 'asta-agent' package along with any other necessary dependencies such as Flask for the web interface.

Step 2: Define the basic structure of your application
- Create a main Python file for initializing the 'asta-agent' server.
- Develop a separate module for handling user inputs and generating responses.

Step 3: Implement the core functionality
- Utilize 'asta-agent' to set up a server that can handle incoming requests from clients.
- Integrate a pre-trained language model or use a simple rule-based system for generating responses based on user inputs.
- Ensure the server can receive messages from clients, process them through the chosen model, and return appropriate responses.

Suggested Features:
- User authentication to track individual conversation histories.
- Integration with a database to store chat logs for analysis or auditing purposes.
- Support for multiple languages to cater to a diverse user base.
- Customizable response generation logic allowing users to tweak the bot's behavior.

How 'asta-agent' is utilized:
- Use 'asta-agent' to abstract away the complexities involved in setting up an AI agent server, focusing more on customizing the application's logic and user experience.
- Leverage 'asta-agent' APIs for managing server operations, such as starting/stopping the server, handling connections, and processing requests efficiently.

💬 Discussion Feed

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