🤖 AI Analysis

Final verdict: SUSPICIOUS

The package LDAQ v1.3.0 has a moderate risk score due to potential code obfuscation and sparse metadata, which raises suspicion but does not conclusively indicate malicious intent.

Potential code obfuscation using pickle.loads
Sparse and possibly inactive author metadata

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external communication.
Shell: No shell execution detected, indicating no immediate risk of unauthorized command execution.
Obfuscation: The use of pickle.loads indicates potential obfuscation or code injection risks, but it may also be used for legitimate purposes like serialization in some applications.
Credentials: No direct evidence of credential harvesting is present.
Metadata: The author's information is sparse and the account seems new or inactive, raising some concerns.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

ng pickle: function = pickle.loads(ser_function) time_start = time.time()

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ 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: fs.uni-lj.si>

Very short email domain: fs.uni-lj.si>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ladisk/LDAQ appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 LDAQ

Create a real-time data monitoring and analysis tool using the LDAQ Python package. This application will allow users to collect sensor data from various sources, visualize it in real-time, and perform basic statistical analysis on the collected data.

Step 1: Set up your environment by installing LDAQ and any necessary dependencies.
Step 2: Design the user interface where users can select the type of sensors they want to connect (e.g., temperature, humidity, light).
Step 3: Implement data acquisition functionality using LDAQ to pull data from these sensors continuously.
Step 4: Integrate live visualization features to display the incoming data streams graphically.
Step 5: Add basic analytical tools such as calculating averages, maximums, minimums, and standard deviations for each data stream.
Step 6: Allow users to save collected data into CSV files for further offline analysis.

Suggested Features:
- Support multiple types of sensors (temperature, humidity, light).
- Enable users to customize the sampling rate for data collection.
- Provide options to switch between different visualization modes (line graphs, bar charts, etc.).
- Include alerts for when certain thresholds are exceeded in the data streams.
- Offer a settings menu to configure additional parameters related to data acquisition and visualization.

How LDAQ is Utilized:
LDAQ will be the backbone of the data acquisition process. Use its capabilities to set up connections to different sensor types, handle data streaming, and integrate live visualizations directly within your application.