T08 · Insecure Dependencies
Note
- Location
- SKILL.md:22
- Finding
- Unpinned Third-Party Dependency Installation## Vulnerability Details **File Location**: `SKILL.md`, line 22 **Vulnerability Type**: Unpinned dependency installation **Risk Level**: Low ### Vulnerable Code ```bash pip install --user matplotlib # only needed for the weekly chart; everything else is stdlib ``` ### Technical Analysis The setup instructions install `matplotlib` without specifying an audited version or verifying package hashes. Consequently, installation behavior and transitive dependencies can change over time without corresponding changes to the reviewed Skill. The package name is legitimate and installation uses the standard Python package workflow; there is no evidence that the Skill intentionally installs a malicious or typosquatted dependency. Nevertheless, an unpinned installation creates a supply-chain exposure if a future package release or one of its transitive dependencies is compromised. Package installation may execute build backend or installation logic with the privileges of the invoking user. The `--user` option limits installation to the current user's Python environment and does not request administrator privileges. This reduces the potential scope but does not protect the user's files, credentials, or processes from code executed under that account. ### Attack Path 1. An attacker compromises a future `matplotlib` release, a required transitive dependency, or the configured Python package index. 2. A user follows the documented setup command after the compromised release becomes the version selected by `pip`. 3. `pip` downloads the unreviewed package or dependency and runs any applicable build or installation logic as the current user. 4. Malicious installation logic executes with the user's permissions and can access resources available to that account. This attack requires an upstream package, dependency, or package-index compromise; the repository itself does not contain or retrieve a known malicious payload. ### Impact Assess ...[truncated 791 chars]
- Remediation
- ## Remediation Suggestions 1. Pin `matplotlib` and all transitive dependencies to versions that have been reviewed and tested. 2. Place dependencies in a dedicated requirements or lock file rather than installing an unconstrained latest release. 3. Record and enforce package hashes with `pip --require-hashes` to detect unexpected artifact substitution. 4. Example installation workflow: ```bash python3 -m pip install --user --require-hashes -r requirements.txt ``` 5. Generate the locked dependency set from a trusted package index and review updates before refreshing versions or hashes. 6. Prefer an isolated virtual environment so the optional charting dependency cannot alter the user's broader Python environment.