Paper Management System

文献管理系统 - 自动化PDF文献索引、搜索、AI提炼工具。当用户需要管理PDF文献、自动索引、搜索文献、提取元数据时激活。

This package appears to be a legitimate local PDF management tool, but review these before installing: - Notification command risk: The notification feature executes whatever you configure as notification.cmd using a shell. Do not set this to an untrusted URL fetcher or arbitrary command; prefer 'stdout' or a vetted relay program. Treat notification.cmd as able to exfiltrate summaries if misconfigured. - Declaration mismatches: The scripts assume common system utilities (python3, sqlite3 CLI, md5sum) though the skill metadata lists no required binaries. Ensure these are available and run the tool in a controlled environment (container/VM) first. - OpenAI/Anthropic inconsistency: README/requirements mention OpenAI/Anthropic, but the summarizer code does not call those APIs. If you plan to enable networked AI summarization, inspect/modify code to use the provider safely and only supply an API key after review. - Sanity checks: Inspect config.yaml or env vars before running; back up any important PDF folders and the DB; run the scripts with notification disabled initially to confirm behavior; consider running in an isolated user account or container to limit filesystem access. If you want higher confidence, ask the publisher for a canonical repository or provenance (the skill references a GitHub URL); verify that the packaged code matches that upstream source and that any AI/networking calls are explicit and audited.

Type: OpenClaw Skill Name: paper-management-system Version: 2.0.2 The bundle contains a command injection vulnerability in `scripts/ai_summarize.py` within the `send_notification` function, which uses `subprocess.run(shell=True)` to execute a command string constructed from PDF metadata (title and summary). While the tool's logic for indexing and managing academic papers appears legitimate, this implementation flaw allows for potential arbitrary code execution if a processed PDF contains a malicious title or content. No evidence of intentional malice, backdoors, or data exfiltration was found.