web-scraper

The skill consists of a SKILL.md file, a requirements.txt file, and a Python script scripts/scrape.py. The analysis focused on identifying potential security threats across these components.

1. Prompt Injection: No patterns indicative of prompt injection (e.g., 'IMPORTANT: Ignore', role-play injection, developer mode activation) were found in SKILL.md or scripts/scrape.py.

2. Data Exfiltration: The Python script makes HTTP GET requests to user-provided URLs to fetch web content and images. It does not contain any code that reads sensitive local files (like ~/.aws/credentials or ~/.ssh/id_rsa) and subsequently sends them to external, non-whitelisted domains. The network operations are solely for fetching content, not exfiltrating local data.

3. Obfuscation: No obfuscation techniques (e.g., Base64 encoding, zero-width characters, homoglyphs, URL/hex/HTML encoding) were detected in any of the provided files.

4. Unverifiable Dependencies: The requirements.txt specifies requests>=2.31.0 and beautifulsoup4>=4.12.0. These are widely used, well-maintained, and trusted Python libraries. The SKILL.md also links to https://requests.readthedocs.io/, a trusted source. While pip install is an external download, the dependencies themselves are considered low risk due to their trusted status. This is noted as an informational finding.

5. Privilege Escalation: No commands or code patterns (e.g., sudo, chmod +x on system files, chmod 777, service/daemon installation) were found that attempt to acquire elevated privileges.

6. Persistence Mechanisms: There are no attempts to establish persistence (e.g., modifying .bashrc, creating cron jobs, or installing systemd services).

7. Metadata Poisoning: The SKILL.md metadata fields (name, description, homepage) were checked and found to be free of malicious instructions.

8. Indirect Prompt Injection: The skill processes arbitrary web content from user-provided URLs. As with any tool that processes external, untrusted data, there is an inherent risk that malicious instructions embedded within the scraped content could potentially influence an AI agent if that content were subsequently fed back to an LLM. However, the skill itself does not perform this action, and this is a general risk associated with processing external data, not a direct vulnerability in the skill's code.

9. Time-Delayed / Conditional Attacks: No conditional logic or time-based triggers were found that would suggest a delayed or conditional malicious payload.

Conclusion: The skill appears to be safe and performs its stated function without exhibiting malicious behavior. The use of standard Python libraries and adherence to common web scraping practices (like respecting robots.txt by default and rate limiting) indicate a well-intentioned design.