Security audit
Security checks for vulnerabilities and agentic risk
This is a narrow local CSV/Excel scatter-plot skill with ordinary file parsing and image output behavior.
Install only in an environment where reading the selected spreadsheet files and writing generated plot images is acceptable. For stronger supply-chain control, pin or lock pandas, matplotlib, openpyxl, and xlrd before production use.
描述前半部分（从CSV/Excel读取数据、解析Result/Min/Max Limit相关数据）与代码基本一致。但该代码块实际只是一个数据加载器和配置提取器，没有调用任何绘图库，也没有生成散点图或绘制Min/Max参考线，因此与“根据Result行数据生成散点图”这一核心声明存在明显落差。未发现额外危险能力或越权资源访问，主要问题是声明包含了代码中未实现的核心功能。
The manifest description and the entire user-facing documentation are written in Chinese, with no indication that other languages are supported or that the language restriction is intentional for a region-specific use case. Under the policy, forcing a specific language without user opt-in is a natural-language locale violation.
This code file contains natural-language strings that force a specific language/locale for documentation and user-facing text. Under the policy, language constraints should not be imposed without offering a user choice or documenting a justified region-specific scope.
This code forces a specific language for the module docstring, argument help text, examples, and runtime status messages. The file does not provide any user opt-in or locale selection, which matches the policy category for language or locale constraints without user choice.
The module hard-codes Chinese font preferences via matplotlib rcParams, which imposes a specific language/locale presentation choice globally. This is a natural-language locale policy concern because users are not offered an opt-in or alternative locale behavior, and no region-specific justification is documented in the file.
Suspicious Unicode normalization or mixed-script content
The dependency specification uses a lower-bound version range instead of pinning an exact version, which makes builds non-reproducible and can cause different environments to install different releases. This increases supply-chain risk because a vulnerable or incompatible future release could be pulled in without review.
pandas>=1.5.0
matplotlib>=3.5.0
openpyxl>=3.0.0
xlrd>=2.0.0
pandas has a known historical advisory, but because the manifest does not pin a version, it is impossible to determine whether deployed environments will receive a fixed or affected release. This uncertainty is a real security weakness because dependency resolution may select a vulnerable version in some environments.
The matplotlib dependency is unpinned, so installations may resolve to different versions over time. While not directly exploitable by itself, this weakens build integrity and can introduce vulnerable or unexpected package versions into the environment.
pandas>=1.5.0
matplotlib>=3.5.0
openpyxl>=3.0.0
xlrd>=2.0.0
openpyxl is unpinned, which creates uncertainty about which release will actually be installed and whether known security fixes are present. In a skill that reads Excel files, this matters more because parser libraries process attacker-controlled document content and historically have had XML-related issues.
pandas>=1.5.0
matplotlib>=3.5.0
openpyxl>=3.0.0
xlrd>=2.0.0
openpyxl has known XXE-related advisories, and the dependency is not pinned, so there is no assurance that installations will use a patched version. This is more dangerous in this skill because it explicitly reads Excel files, meaning attacker-supplied spreadsheet content could reach a vulnerable parser if an affected version is installed.
The xlrd dependency is specified with only a minimum version, allowing dependency resolution to pull in different future releases. This is a supply-chain hygiene issue that can lead to unreviewed code, regressions, or exposure to newly introduced vulnerabilities.
pandas>=1.5.0
matplotlib>=3.5.0
openpyxl>=3.0.0
xlrd>=2.0.0
No suspicious patterns detected.