🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential obfuscation, which could indicate attempts at hiding functionality, though there are no definitive indicators of malicious behavior such as network activity or shell execution.

High obfuscation risk due to base64 decoding
Single package maintainer, potentially new or less experienced

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating no unexpected command-line operations.
Obfuscation: The presence of multiple base64 decoding operations suggests potential obfuscation, but without context, it's hard to determine malicious intent.
Credentials: No clear patterns indicative of credential harvesting were detected.
Metadata: The maintainer has only one package, indicating they may be new or less active, but no other suspicious activities were flagged.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

[index_result] decoded = base64.b64decode(decoded) return decoded def find_campaign_id(data):
) try: decoded = base64.b64decode(clean_bytes, validate=True) decrypted = xor_data(dec
utf-8") decoded_str = base64.b64decode(matched_string).decode() for item in decoded_str.spl
utf-8") decoded_str = base64.b64decode(match_str) if b"DW" in decoded_str: dat
decoded_str = base64.b64decode(encoded_string) grabber_str = decoded_str[:9
decoded_bytes = base64.b64decode(base64_str, validate=True) encoded_c2 =

✓ 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: capesandbox.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Kevin O'Reilly" 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 CAPE-parsers

Create a malware configuration extraction tool using the CAPE-parsers Python package. Your application should allow users to upload suspected malicious files (such as executables, scripts, etc.) and extract configuration details from these files that might indicate malicious intent or behavior. The application should have a user-friendly interface where users can select a file, initiate the parsing process, and view the extracted configuration data in a structured format such as JSON or XML.

Step-by-Step Guide:
1. Set up a basic Python environment with Flask or Django for the web application framework.
2. Install CAPE-parsers via pip.
3. Create a file upload feature allowing users to submit files for analysis.
4. Integrate CAPE-parsers into your application to parse uploaded files.
5. Display parsed data in a human-readable format on a results page.
6. Optionally, implement additional features like saving parsed data to a database, comparing configurations over time, or generating alerts based on specific criteria.

Features:
- User authentication for accessing the application.
- File upload functionality with validation checks.
- Real-time parsing progress indication.
- Structured output display of parsed data.
- Database integration for storing and retrieving past analyses.
- Alert system for detecting known malicious patterns.

Utilizing CAPE-parsers:
- Use CAPE-parsers to extract configuration information from the uploaded files.
- Ensure that the extracted data includes relevant fields such as command and control server addresses, encryption keys, and other potentially harmful settings.
- Provide options within the application to customize which configurations are extracted.