🤖 AI Analysis

Final verdict: SAFE

The package shows moderate obfuscation which might warrant further investigation, but there is no evidence of malicious activities such as shell execution or credential harvesting.

Moderate obfuscation risk due to base64 decoding.
No shell execution or credential risk detected.

Per-check LLM notes

Network: The observed network calls are typical for packages that interact with external services or APIs.
Shell: No shell execution patterns detected.
Obfuscation: The presence of base64 decoding indicates potential obfuscation but could also be for legitimate data handling purposes.
Credentials: No clear patterns indicative of credential harvesting were detected.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

url, params): resp = requests.get(url, params=params, auth=self.auth) if resp.status_c
rid}/{folder}" resp = requests.get(url, auth=self.auth) if resp.status_code != 200:
serid}/{path}" resp = requests.post(url, json=json, auth=self.auth) if resp.status_code
src, 'rb') resp = requests.put(url, data=data, headers={'Content-Type': type})
serid}/{path}" resp = requests.head(url, auth=self.auth) if resp.status_code == 404:
d}/{new_path}" resp = requests.post(url, json=json, auth=self.auth) if resp.status_code

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

base64decode(str): str = base64.b64decode(str) return str.decode('ascii') if isinstance(str, bytes
else: model.eval() # Set model to evaluate mode running_loss =
ng = model.training model.eval() images_so_far = 0 fig = plt.figure() with torc
it='val'): self.model.eval() running_corrects = 0 confusion_matrix = np

✓ 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: abraiasoftware.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository abraia/abraia-multiple appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Jorge Rodriguez Araujo" 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 abraia

Create a Python-based image recognition app that leverages the Abraia Python SDK to analyze and categorize images uploaded by users. This app will serve as a tool for users to understand the content of their images better and could be particularly useful for organizing photo libraries or identifying objects within images.

**Core Features:**
1. **Image Upload:** Users should be able to upload images from their local device or provide a URL to an image hosted online.
2. **Real-time Analysis:** Upon uploading, the app should use the Abraia SDK to perform real-time analysis on the image, identifying key elements such as objects, scenes, and activities present in the picture.
3. **Detailed Categorization:** Display a categorized breakdown of the recognized elements, including probabilities associated with each identification.
4. **User Interface:** Develop a simple yet effective web interface using Flask or Django, allowing users to interact with the app easily.
5. **Error Handling:** Implement robust error handling to manage issues like invalid uploads or connectivity problems with the Abraia service.
6. **Security Measures:** Ensure that all user data and interactions are handled securely, with appropriate measures in place to protect privacy.

**How to Use the 'abraia' Package:
- Initialize the SDK and authenticate your app with the necessary API keys provided by Abraia.
- Use the SDK's methods to send images for analysis, handling both local files and remote URLs.
- Parse and display the results returned by the Abraia service, showcasing them in a user-friendly format within the web interface.

This project not only integrates modern AI capabilities but also enhances user experience through intuitive design and functionality.