Automatically downloads student homework from QQ email, unzips files, performs AI grading based on templates, and generates an Excel grade report.

This skill mostly does what it says, but there are several red flags you should resolve before using it with real student data or credentials: - Undeclared OpenAI usage: The grader reads student code and calls OpenAI via OPENAI_API_KEY, but that key is not declared in the skill metadata and not listed in requirements.txt. Ask the author to declare OPENAI_API_KEY as a required credential and add the openai package to requirements. - External data exfiltration: Student source files are sent to an external AI provider (OpenAI). If student code is sensitive/personal, obtain consent or avoid sending it externally. - Executing untrusted code: The skill runs student .py files with subprocess.run. This can run arbitrary malicious code. Only run in an isolated, ephemeral sandbox/container with no sensitive network/filesystem access. - Unsafe zip extraction: zip_ref.extractall is used without sanitizing paths (zip-slip). Ensure filenames are sanitized or extract inside a strict sandbox. - Missing templates/resources: template_manager returns templates/assignment_{id}, but no templates are bundled. Confirm where templates come from and ensure they are trustworthy. Recommended actions before installing or running: 1) Require the author to update skill.yaml to declare OPENAI_API_KEY and add openai to requirements.txt; document that student code will be sent to OpenAI. 2) Run the skill in a locked-down container or VM (no network or limited network) until you validate behavior. 3) Inspect or provide the templates directory, and consider local-only grading if you cannot allow external AI calls. 4) Patch code to validate zip entries and avoid path traversal; do not run student code on the host—use sandboxing/time/resource limits and consider static analysis instead of execution. 5) If you must use it, use a throwaway QQ account and rotate its auth code afterwards. Given these gaps and risky behaviors, treat the skill as suspicious until the author addresses the above points.

Type: OpenClaw Skill Name: homework-gader-skill Version: 1.0.0 The skill bundle contains critical security vulnerabilities that facilitate Remote Code Execution (RCE) and file system compromise. Specifically, `modules/grader.py` implements a function to execute arbitrary Python code from untrusted email attachments using `subprocess.run`, and `modules/extractor.py` uses `zip_ref.extractall()` without path validation, making it vulnerable to Zip Slip attacks. While these functions appear intended for automated homework grading, the lack of sandboxing or input sanitization allows an external attacker (via email) to execute malicious code on the agent's host system.