🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits multiple high-risk characteristics including potential code obfuscation, risky shell execution, and indications of low-effort metadata that may suggest typosquatting. However, there is no concrete evidence of malicious activity or credential harvesting.

High obfuscation and shell risks
Potential typosquatting attempt

Per-check LLM notes

Network: The use of network calls to external URLs may indicate legitimate functionality but could also be used for unexpected data exchange.
Shell: Executing commands via shell may serve a valid purpose but poses higher risk due to potential command injection vulnerabilities.
Obfuscation: The use of eval and dynamic importation suggests potential code obfuscation to hide functionality, which is risky.
Credentials: No clear patterns indicating credential harvesting were found.
Metadata: The package shows some signs of low effort and potential typosquatting, but lacks definitive evidence of malicious intent.
⚠ Typosquatting target: doit

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

uête POST response = requests.get(url, headers=headers) # Vérification de la répons
uête POST response = requests.post(url, headers=headers, data=json.dumps(data), stream=True)
self.session = session or requests.Session() self.session.headers.update({ "User-

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

try: if eval(new_regle): a_recuperer[i] = 1
h_to_execute)) le_module=__import__(Path(path_to_execute).stem) le_module.python_post_install() def Get

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

file}" process = subprocess.run(curl_command, shell=True, capture_output=True, text=True)
. """ process_get = subprocess.run( ["curl", "-s", "-L", url], capture_output
son_path}" process = subprocess.run(curl_command, shell=True, capture_output=True, text=True)
llow redirects process = subprocess.run( ["curl", "-s", "-L", json_url], capture_o
mport subprocess subprocess.run(["open", path]) return elif sys.platfo
ment multi -e return subprocess.run( ["osascript", "-e", script], text

✓ Credential Harvesting

No credential harvesting patterns detected

⚠ Typosquatting score 3.0

Possible typosquat of: doit

"aait" is 2 edit(s) from "doit"

✓ 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 4.0

2 maintainer concern(s) found

Author "Orange community" 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 aait

Create a Python-based mini-application that leverages the 'aait' package to enhance user interaction through advanced AI functionalities within the Orange data visualization platform. This application will serve as a bridge between users and complex datasets, providing intuitive insights and predictions. Here’s a step-by-step guide on how to build this application:

1. **Setup Environment**: Ensure you have Python installed along with Orange and the 'aait' package. Use pip to install any necessary dependencies.
2. **Project Structure**: Organize your project into modules such as DataLoading, AIProcessing, Visualization, and UserInterface.
3. **Data Loading**: Develop a feature within the DataLoading module that allows users to upload their dataset directly into the application. Utilize Orange's capabilities to handle various file formats and ensure seamless data ingestion.
4. **AI Processing**: Implement the AIProcessing module where 'aait' comes into play. Use 'aait' to preprocess the data, perform feature selection, and apply machine learning models to predict outcomes based on the uploaded dataset. Focus on using 'aait' to streamline these processes and make them more accessible.
5. **Visualization**: Create a Visualization module that integrates Orange's powerful visualization tools. This module should display the results of the AI processing in a user-friendly manner, including charts, graphs, and interactive visualizations that help users understand the predictive model's performance.
6. **User Interface**: Design a simple yet effective UserInterface using Python’s Tkinter or another preferred GUI framework. This interface should allow users to navigate through the different functionalities of your application, from data loading to visualization of results.
7. **Interactive Predictions**: Enhance the application by allowing users to input new data points and receive real-time predictions based on the trained model. This feature should leverage 'aait' for quick and accurate predictions.
8. **Documentation & Testing**: Write comprehensive documentation explaining how each part of the application works and why certain decisions were made regarding the use of 'aait'. Additionally, conduct thorough testing to ensure the application runs smoothly and provides reliable results.
9. **Deployment**: Prepare the application for deployment either as a standalone executable or as a web service, ensuring it is accessible to users who may not have extensive technical knowledge.

Throughout the development process, focus on making the application intuitive and user-friendly, ensuring that even those with limited AI expertise can benefit from advanced analytics.