🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of potential tampering due to its metadata indicating recent and unusual creation patterns, along with some level of obfuscation through base64 encoding. These factors suggest caution and further investigation.

Unusual metadata indicating recent and possibly suspicious creation
Base64 obfuscation hinting at possible hidden code logic

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, reducing likelihood of malicious activities like backdoors.
Obfuscation: Base64 decoding may indicate an attempt to hide code logic, but could also be used for standard data encoding/decoding purposes.
Credentials: No clear patterns of credential harvesting detected.
Metadata: The package shows signs of being newly created with unusual commit patterns, suggesting potential suspicious activity.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

self.value = base64.b64decode(res_value) _logger.debug("property {}:
bytes = base64.b64decode(res_value) self.value = int.from_bytes(

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: orange.fr>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks
All 9 commits happened within 24 hours

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
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 DarkBridge

Create a photo management utility called 'PhotoSyncer' using the Python package 'DarkBridge'. This utility aims to streamline the workflow of photographers by automatically transferring metadata from Nikon sidecar files (.NEF.NEF) to Darktable, a free and open-source raw image editor. The utility should be user-friendly, efficient, and capable of handling large volumes of photos.

Step 1: Set up the environment
- Ensure Python is installed on your system.
- Install DarkBridge via pip.
- Configure Darktable to accept incoming metadata changes.

Step 2: Design the main functionality
- Develop a function to scan a directory for .NEF.NEF files.
- Use DarkBridge to parse these sidecar files and extract metadata.
- Integrate with Darktable's API or command-line interface to apply the extracted metadata to the corresponding raw images.

Step 3: Implement additional features
- Add support for batch processing of multiple directories.
- Include an option to preview changes before applying them to Darktable.
- Implement logging to track processed files and any errors encountered.
- Allow users to customize which metadata fields are transferred.

Step 4: User Interface
- Create a simple GUI using Tkinter or PyQt for selecting directories and viewing logs.
- Ensure the GUI is responsive and provides clear feedback during operations.

How to Utilize DarkBridge:
- Use DarkBridge to read Nikon sidecar files efficiently without needing complex parsing logic.
- Leverage DarkBridge's capabilities to bridge the gap between proprietary Nikon sidecar formats and the more open Darktable environment.
- Explore DarkBridge's documentation to understand its limitations and best practices.