T08 · Insecure Dependencies
Warning
- Location
- SKILL.md:15
- Finding
- Unpinned Third-Party Dependencies Permit Supply-Chain Drift<![CDATA[ ## Vulnerability Details **File Location**: `SKILL.md`, lines 15-30 **Vulnerability Type**: Unpinned dependency installation **Risk Level**: Medium ### Vulnerable Code ```yaml install: - kind: uv package: biopython bins: [] - kind: uv package: pandas bins: [] - kind: uv package: scikit-learn bins: [] - kind: uv package: matplotlib bins: [] - kind: uv package: numpy bins: [] ``` ### Technical Analysis The installation metadata requests five third-party packages without exact versions, integrity hashes, or a lockfile. Consequently, each installation may resolve to different package artifacts than those examined during this audit. Although the dependency section elsewhere in the documentation mentions minimum versions, those prose constraints are not enforced by the installation configuration. Even minimum-version constraints would not provide reproducible or integrity-verified installation. This creates a supply-chain exposure if a package registry account, package release, dependency resolution path, or transitive dependency is compromised. Python package installation may execute build backend code, while malicious runtime code may execute when the installed modules are imported. ### Attack Path 1. An attacker compromises a listed package or one of its transitive dependencies and publishes a malicious release. 2. A user installs the Skill using the declared `uv` installation entries. 3. Because no exact version or hash is specified, the resolver selects the malicious or otherwise unreviewed release. 4. Malicious logic executes during package building, installation, or later import by `equity_scorer.py`. 5. The code runs with the privileges and filesystem/network access of the user performing the installation or analysis. ### Impact Assessment Successful exploitation could execute arbitrary code under the installing or invoking user's account. The resulting access could include reading or modify ...[truncated 320 chars]
- Remediation
- <![CDATA[ ## Remediation Suggestions 1. Pin each direct dependency to a reviewed exact version, such as `numpy==<reviewed-version>`. 2. Generate and commit a lockfile that also fixes all transitive dependency versions. 3. Require cryptographic hashes for downloaded distributions and reject artifacts whose hashes do not match. 4. Prefer reviewed binary wheels from a trusted package index, and explicitly restrict the allowed package index. 5. Configure installation to reject unexpected source distributions where practical, reducing exposure to build-time code execution. 6. Use automated dependency vulnerability and provenance scanning in CI. 7. Periodically update dependencies through a controlled review process rather than resolving unrestricted current releases during installation. ]]>