🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to network and shell execution vulnerabilities, though it does not show clear signs of malicious intent. The combination of these factors warrants further scrutiny.

High shell execution risk
Moderate network call risk

Per-check LLM notes

Network: The network calls may be legitimate if the package requires external API access, but could also indicate data exfiltration.
Shell: Subprocess execution can be risky, especially when invoking commands like 'powershell' or 'rocm-smi', suggesting potential for executing arbitrary code which might be intended for malicious purposes.
Obfuscation: The use of base64 decoding to write files could indicate obfuscation but is also common in legitimate scenarios such as handling encrypted data.
Credentials: No patterns indicative of credential harvesting were found.
Metadata: The author has only one package, which might indicate a new or less active account, but there are no other red flags.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

try: with socket.create_connection((host, port), timeout=1): return True
tps://")): response = requests.get(image_path, timeout=5) if response.status_code == 20

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

f.write(base64.b64decode(data_b64)) logger.info(f"File saved: {fi

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

] huey_process = subprocess.Popen(huey_cmd, env=child_env) logger.info(f"Started exter
try: result = subprocess.run( [ "rocm-smi",
try: result = subprocess.run( [ "powershell",

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: dcc.uchile.cl

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository DashAISoftware/DashAI appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "DashAI Team" 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 DashAI

Create a mini-application that allows users to train, evaluate, and deploy machine learning models using the DashAI package. This application will serve as a simple GUI-based platform where users can upload their datasets, select from a variety of pre-configured machine learning models, and perform tasks such as model training, evaluation, and prediction without needing extensive knowledge about the underlying algorithms.

Steps:
1. Set up the basic structure of your application using DashAI's framework.
2. Implement a dataset upload feature allowing users to load CSV files into the application.
3. Integrate DashAI's model selection interface where users can choose from different types of machine learning models (e.g., classification, regression).
4. Develop a model training module that utilizes DashAI's training functionalities to train selected models on uploaded datasets.
5. Add an evaluation component that uses DashAI’s evaluation tools to assess the performance of trained models.
6. Include a deployment section that allows users to save and export trained models for future use.
7. Ensure all interactions within the application are user-friendly and provide feedback at each step of the process.

Features:
- User-friendly GUI for easy interaction
- Support for multiple file formats for dataset uploads
- Pre-configured options for various types of machine learning models
- Real-time feedback during training and evaluation processes
- Export functionality for trained models

Utilization of DashAI:
DashAI simplifies the entire pipeline from data preprocessing to model deployment. It provides a set of graphical tools that streamline the process of selecting models, training them, evaluating their performance, and deploying them. Your task is to leverage these tools to create an intuitive interface that abstracts away much of the complexity involved in machine learning, making it accessible to users who may not have deep technical expertise.