Pipenet-skill

分析和处理TOML格式的管道网络，包括转换描述、加载求解及结构与运行状态可视化。

High-level advice before installing or enabling this skill: - Code–purpose match: The Python modules implement the declared functionality (TOML loader, numerical solver, visualizer). That is coherent with the skill description. - Prompt-injection risk: The SKILL.md was flagged for base64 and Unicode control characters. Inspect SKILL.md raw text for hidden characters or encoded payloads before trusting it. Remove or sanitize any suspicious hidden content. - File I/O risks: The skill reads TOML files from arbitrary paths and writes output files to ./src/toml and ./src/html using names derived from input (pipe_net.name and scenario_name). If an attacker controls the TOML content or names, they could cause directory traversal or overwrite files. Mitigations: run in a sandboxed environment, restrict and validate file paths, and sanitize file names (reject '..', absolute paths, or suspicious characters). - Input validation: Several functions assume correct types (e.g., float values for some scenario actions). Malformed inputs can raise exceptions or cause partial failures—validate inputs before handing them to the skill. - External resources: Generated HTML references external CDNs for JS/CSS. The HTML itself does not exfiltrate data, but viewing it in a browser will fetch remote resources. If you must avoid external network calls, host the JS/CSS locally or remove CDN references. - Dependencies & runtime: The skill expects Python >=3.11 (uses tomllib) and scientific packages (numpy, scipy). Ensure the runtime environment can install/contain those packages and that they are acceptable for your security posture. - Testing: Before using on real data or giving it file-system access, run the skill in an isolated container, pass controlled TOML files, and verify it cannot read or write outside an allowed directory. If you want, I can: (1) locate and show the exact lines in SKILL.md that contain the hidden characters, (2) suggest code changes to sanitize file paths and names, or (3) propose a minimal sandbox policy for running this skill safely.

Type: OpenClaw Skill Name: pipenet-skill Version: 1.0.0 The skill bundle contains path traversal vulnerabilities in `src/skill.py` and `src/core/visualizer.py`, where the `pipe_net.name` attribute from user-provided TOML content is used to construct file paths for writing (`.toml` and `.html` files) without sanitization. While the code appears to be a legitimate engineering tool for fluid network simulation using `scipy` and `networkx`, these vulnerabilities could allow an attacker to write files outside the intended directories. Additionally, the bundle includes large minified third-party JavaScript libraries (`src/core/lib/vis-9.1.2/vis-network.min.js`), which are standard for visualization but difficult to verify for integrity.