ag2

v0.13.3 suspicious
6.0
Medium Risk

A programming framework for agentic AI

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant risks due to its network and shell command execution capabilities, suggesting potential unauthorized interactions or code execution. However, no clear malicious intent has been confirmed.

  • High network risk
  • High shell execution risk
Per-check LLM notes
  • Network: The presence of network calls to external URLs suggests potential data exfiltration or interaction with external services, which could be unexpected and risky.
  • Shell: Executing shell commands, especially those querying the PowerShell version, may indicate an attempt to execute code on the user's machine, potentially leading to unauthorized access or behavior.
  • Obfuscation: The presence of base64 decoding and code compilation suggests potential obfuscation, but it could also be part of normal functionality like handling encoded images or executing user-defined scripts.
  • Credentials: No clear patterns indicative of credential harvesting were found.
  • Metadata: The author's information is incomplete and the account seems new or inactive, which raises some suspicion but not conclusive evidence of malice.
  • ⚠ Typosquatting target: arq

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • eout", 30) response = requests.get(self.bing_base_url, **request_kwargs) response.raise
  • he URL response = requests.get(url, **request_kwargs) response.raise_for_status
  • } try: response = requests.get(url, stream=True, headers=custom_headers, timeout=30)
  • A URL file response = requests.get(image_file, timeout=30) content = BytesIO(response.c
  • } response = requests.post( config["base_url"].rstrip("/") + "/worker_gener
  • y: async with httpx.AsyncClient(timeout=self._timeout) as client: respon
⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • 4 string image_data = base64.b64decode(base64_image) # Check the first few bytes for known
  • return Image.open(BytesIO(base64.b64decode(data))) @require_optional_import("PIL", "unknown") def mes
  • python code try: compile(code, "test", "exec") return "python" except SyntaxError: #
  • try: compile(code, "<string>", "exec") print("successfully compiled")
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • (): try: result = subprocess.run(["powershell", "$PSVersionTable.PSVersion.Major"], capture_o
  • try: result = subprocess.run( ["pwsh", "-Command", "$PSVersionTable.PSVer
✓ Credential Harvesting

No credential harvesting patterns detected

⚠ Typosquatting score 3.0

Possible typosquat of: arq

  • "ag2" is 2 edit(s) from "arq"
✓ Registered Email Domain

Email domain looks legitimate: ag2.ai>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ag2ai/ag2 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 ag2 
Your task is to develop a simple yet functional agent-based chatbot using the 'ag2' package, which is designed to facilitate the creation of agentic AI systems. This chatbot will be capable of understanding user queries, performing actions based on those queries, and providing responses that reflect its understanding of the task it has been given. Here’s a step-by-step guide on how to approach this project:

1. **Project Setup**: Start by setting up your development environment. Ensure you have Python installed and create a new virtual environment for this project. Install the 'ag2' package via pip.

2. **Understanding User Queries**: Implement a basic natural language processing (NLP) system within your chatbot using 'ag2'. This system should be able to parse user inputs and determine the intent behind each query. For instance, if a user asks about the weather, your chatbot should recognize that the intent is to obtain weather information.

3. **Action Execution**: Once the intent is recognized, use 'ag2' to define actions that correspond to different intents. For example, if the intent is related to weather, your chatbot should fetch current weather data from an API like OpenWeatherMap. Use 'ag2' to structure these actions as tasks that the agent can perform.

4. **Response Generation**: After executing the necessary action, generate a response that accurately reflects the outcome of the action. If the action was fetching weather data, the response should include the relevant weather details.

5. **Enhanced Features**: To make your chatbot more engaging, consider adding the following features:
   - **Contextual Memory**: Allow your chatbot to remember previous interactions with the same user to provide more personalized responses.
   - **Multiple Intent Handling**: Enable the chatbot to handle multiple intents in a single interaction, such as asking about both the weather and news.
   - **Feedback Loop**: Implement a mechanism where users can give feedback on the chatbot’s responses, which can be used to improve future interactions.

6. **User Interface**: Finally, design a simple command-line interface (CLI) for interacting with your chatbot. Alternatively, you could explore integrating it into a web application or mobile app for broader accessibility.

Throughout the development process, leverage 'ag2' to streamline the creation and management of agents, their tasks, and interactions. Your goal is to demonstrate the flexibility and power of 'ag2' in building intelligent, interactive applications.