🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits behaviors that suggest potential misuse, including network risk due to user-agent spoofing and shell risk from executing external commands without clear documentation.

Network risk due to user-agent spoofing
Execution of external commands with unclear purposes

Per-check LLM notes

Network: Network calls could be legitimate for fetching resources, but the use of User-Agent spoofing might indicate attempts to bypass detection or access restricted content.
Shell: Execution of external commands like 'git', 'xdotool', and running other scripts directly may pose risks, especially if these actions are not clearly documented and serve no apparent benign purpose within the package's functionality.
Obfuscation: The presence of base64 decoding and unquoting suggests potential code obfuscation or encryption handling, which could be used for malicious purposes.
Credentials: No clear patterns indicative of credential harvesting were found.
Metadata: The package shows low effort in metadata and maintainer history, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (6723 chars)

○ 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

385 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)

try: req = urllib.request.Request(src, headers={"User-Agent": "Mozilla/5.0"})
a/5.0"}) with urllib.request.urlopen(req, timeout=15) as resp: data =
time.time())}" req = urllib.request.Request(src, headers={"User-Agent": "Mozilla/5.0"})
"Mozilla/5.0"}) with urllib.request.urlopen(req, timeout=30) as resp: data = resp.re
t = None): async with httpx.AsyncClient( follow_redirects=True, timeout=30, proxy=_proxy
deepseek.com" async with httpx.AsyncClient(timeout=120.0) as client: resp = await client.post(

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

.unquote( base64.b64decode(url_match.group(1)).decode() ) e
ey_b64: raw = base64.b64decode(encrypted_key_b64) # On Linux, the key is pr
"]) _trajectory_model.eval() _trajectory_tags = checkpoint.get("tags", [])
"model_state"]) model.eval() logger.info("trajectory_model_loaded", extra={"pat
t not in os.sys.path: __import__("sys").path.insert(0, _root) # Override browser_use with local ve
oject_root / "vendor") _sys = __import__("sys") if _vendor_path not in _sys.path: _sys.path.insert(0, _

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

git_root = ( subprocess.check_output( ["git", "rev-parse", "--show-toplevel"],
"python_bridge") subprocess.Popen( [sys.executable, server_path],
process result = subprocess.run( ["xdotool", "search", "--class", "chrome",
n")[0] geom = subprocess.run( ["xdotool", "getwindowgeometry", "--she
chrome_path: subprocess.Popen( [ chrome_path,
git_root = ( subprocess.check_output( ["git", "rev-parse", "--show-toplevel"]

✓ Credential Harvesting

No credential harvesting patterns detected

✓ 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 aibrowser

Create a Python-based web scraping tool called 'AutoScrape' that leverages the 'aibrowser' package for intelligent browser automation. The goal of AutoScrape is to automate the process of extracting structured data from various websites, making it easier for users to gather information without manually navigating through each site. Here’s a detailed breakdown of the project requirements and functionalities:

1. **Setup and Initialization**: Start by setting up a virtual environment and installing necessary packages including 'aibrowser'. Ensure you have the latest version of the package installed.
2. **Core Functionality**:
   - **URL Input**: Allow users to input URLs they wish to scrape.
   - **Data Extraction**: Use 'aibrowser' to navigate to the specified URL, extract relevant data based on user-defined criteria (e.g., product names, prices, descriptions), and format this data into a structured format like JSON or CSV.
3. **Advanced Features**:
   - **Dynamic Content Handling**: Implement a feature that can handle dynamically loaded content using 'aibrowser', ensuring all content is accessible even if it's not immediately visible on page load.
   - **Multi-Page Scraping**: Develop functionality that can follow pagination or infinite scrolling mechanisms to scrape multiple pages within a single website.
4. **User Interface**:
   - **CLI Interface**: Provide a command-line interface where users can interact with the tool, input URLs, and specify extraction criteria.
5. **Error Handling and Logging**:
   - **Robust Error Handling**: Include error handling to manage issues such as network errors, invalid URLs, and inaccessible content.
   - **Logging**: Implement logging to keep track of the scraping process, including start times, end times, any errors encountered, and the success rate.
6. **Security Considerations**:
   - **Respect Robots.txt**: Ensure the scraper respects the 'robots.txt' file of each website it scrapes, adhering to the rules set by website owners.
   - **Rate Limiting**: Implement rate limiting to avoid overwhelming servers with too many requests in a short period.
7. **Documentation and Testing**:
   - **Detailed Documentation**: Write comprehensive documentation explaining how to install and use the tool, including examples.
   - **Testing**: Conduct thorough testing to ensure reliability and accuracy of data extraction across different types of websites.

This project aims to showcase the capabilities of the 'aibrowser' package while providing a practical solution for automated web scraping tasks.