aesdk

v0.1.3 safe
4.0
Medium Risk

Agentic Econometrics SDK

🤖 AI Analysis

Final verdict: SAFE

Based on the analysis, the package appears to have legitimate uses with low risks associated with network calls, shell executions, obfuscation, and credentials. There are no clear indications of malicious activity or supply-chain attacks.

  • Low credential risk
  • No suspicious network activities
  • Potential legitimate use of shell executions
Per-check LLM notes
  • Network: Network calls seem to be for legitimate purposes like fetching DOIs and posting data, but could indicate external dependency on services.
  • Shell: Shell executions might be used for tasks like converting PDFs to text or interacting with git, which could be part of the package's functionality but should be scrutinized for potential misuse.
  • Obfuscation: The observed patterns likely represent legitimate cryptographic operations involving base64 decoding and signature verification.
  • Credentials: No suspicious patterns indicative of credential harvesting were identified.
  • Metadata: The author has only one package, which could indicate a new or less active account, but no other red flags are present.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • try: response = requests.post(url, json=payload, headers=headers, timeout=self.timeout_sec
  • e try: response = requests.head( f"https://doi.org/{doi}", allow_red
  • 429}: response = requests.get( f"https://doi.org/{doi}", a
  • } try: response = requests.post(url, json=payload, headers=headers, timeout=timeout_seconds)
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • IGEST", Signature=base64.b64decode(signature), SigningAlgorithm="RSASSA_PSS_SHA_256
  • c_key.verify( base64.b64decode(signature), bytes.fromhex(blob_sha256),
  • hex(blob_sha256), base64.b64decode(signature), ) return bool(result.is_valid)
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • if pdftotext: subprocess.run([pdftotext, "-layout", str(task), str(target)], check=False,
  • one try: result = subprocess.run([executable, *args], check=False, capture_output=True, text=
  • ne: try: result = subprocess.run(["git", *args], cwd=str(cwd), check=False, capture_output=Tr
  • [] try: result = subprocess.run([executable, "--list-extensions", "--show-versions"], check=
  • tions": [], } probe = subprocess.run( [sys.executable, "-m", "aesdk", "methods", "list"],
  • try: proc = subprocess.run( [sys.executable, str(execution_path)],
✓ 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

Repository ajolex/aesdk appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AESDK Contributors" 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 aesdk 
Create a Python-based mini-application that integrates the 'aesdk' package to perform advanced econometric analysis on financial market data. Your application will be named 'EcoAnalyzer'. Here’s a detailed breakdown of the project requirements:

1. **Project Setup**: Initialize a new Python virtual environment and install the required packages including 'aesdk'. Ensure you have access to real-time or historical financial market data.

2. **Data Collection**: Utilize APIs from financial services like Alpha Vantage or Yahoo Finance to collect stock price data. The application should allow users to input a ticker symbol and date range for data retrieval.

3. **Data Preprocessing**: Implement data cleaning and normalization techniques using 'aesdk'. This includes handling missing values, removing outliers, and transforming data into a suitable format for analysis.

4. **Econometric Analysis**: Apply various econometric models such as ARIMA, GARCH, or other time series forecasting methods available in 'aesdk' to predict future trends based on historical data. Allow users to choose the model they want to apply.

5. **Visualization**: Integrate visualization libraries like Matplotlib or Plotly to graphically represent the analyzed data and predictions. Users should be able to view the historical data alongside the predicted trends.

6. **User Interface**: Develop a simple command-line interface (CLI) or a basic web interface using Flask/Django where users can interact with the application, input parameters, and see results.

7. **Documentation & Testing**: Write comprehensive documentation detailing how to use the 'EcoAnalyzer', including setup instructions and examples. Conduct thorough testing to ensure the application functions correctly across different datasets and scenarios.

The goal is to create a tool that not only leverages the power of 'aesdk' but also provides valuable insights into financial markets through user-friendly interfaces and robust analytical capabilities.