AI Analysis
Final verdict: SUSPICIOUS
The package shows some signs of potential risk, particularly with subprocess calls that could be used for legitimate purposes but also pose a threat if misused. While there's no clear evidence of malicious intent, the combination of shell risk and metadata concerns warrants further scrutiny.
- High shell risk due to subprocess calls
- Some metadata concerns
Per-check LLM notes
- Network: The network call is likely for weather information retrieval, which seems benign.
- Shell: Subprocess calls to pip and venv could be legitimate for package management or environment setup but may also indicate potential execution of arbitrary commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
- Metadata: Some non-secure HTTP links and a new maintainer account suggest potential risk, but no clear signs of malicious intent or typosquatting.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
} try: resp = requests.get(f"https://wttr.in/{city_name}?format=j1") resp.raise
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 10.0
Found 5 shell execution pattern(s)
ip...") pip_upgrade = subprocess.run( [python_path, "-m", "pip", "install", "--upgrad{req}") result = subprocess.run( [python_path, "-m", "pip", "install", req],venv_result = subprocess.run( [sys.executable, "-m", "venv", venv_pat# ๆง่กไปฃ็ result = subprocess.run( cmd, input=stdin_data,try: process = subprocess.Popen( [python_path, script_path],
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
score 3.0
Suspicious email domain flags: Very short email domain: qq.com
Very short email domain: qq.com
Suspicious Page Links
score 10.0
Found 19 suspicious link(s) on the package page
Non-HTTPS external link: http://hq.sinajs.cn/list=[stock_codeNon-HTTPS external link: http://hq.sinajs.cn/list=sh600519Non-HTTPS external link: http://money.finance.sina.com.cn/quotes_service/api/json_v2.php/CN_MarketData.geNon-HTTPS external link: http://money.finance.sina.com.cn/quotes_service/api/json_v2.php/CN_MarketData.geNon-HTTPS external link: http://your_base_url/v1Non-HTTPS external link: http://hq.sinajs.cn/list=[่ก็ฅจไปฃ็
Git Repository History
Repository wanxingai/LightAgent appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "caiweige" 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 LightAgent
Create a collaborative task management mini-app using the LightAgent Python package. This app will facilitate team collaboration by enabling multiple agents to work together on tasks, share information, and learn from each other. Each agent will represent a team member and will have its own set of responsibilities and capabilities. The app should include the following features: - Task assignment: Assign tasks to different agents based on their skills and availability. - Progress tracking: Track the progress of each task and update the status accordingly. - Communication: Allow agents to communicate with each other about task-related information. - Learning mechanism: Implement a learning feature where agents can improve their performance based on feedback and previous experiences. - Visualization: Provide a user-friendly interface to visualize the progress and status of all tasks. To utilize the LightAgent package, follow these steps: 1. Initialize the environment: Set up the environment by installing the LightAgent package and initializing the agents with their respective roles and skills. 2. Define tasks: Create a list of tasks that need to be completed, specifying the required skills and deadlines. 3. Assign tasks: Use the LightAgent framework to assign tasks to the appropriate agents based on their capabilities and current workload. 4. Monitor progress: Continuously monitor the progress of each task and update the status as tasks are completed. 5. Facilitate communication: Enable agents to communicate with each other about any issues or updates related to their tasks. 6. Implement learning: Use the self-learning feature of LightAgent to help agents improve their performance over time based on feedback and outcomes. 7. Visualize data: Integrate a visualization tool to display the overall progress and status of all tasks in a clear and understandable manner.