T08 · Insecure Dependencies
Warning
- Location
- requirements.txt:1
- Finding
- Unpinned and Unverified Third-Party Dependencies## Vulnerability Details **File Location**: `requirements.txt:1-3` **Vulnerability Type**: Unpinned and unverified package dependencies **Risk Level**: Medium **Complete Code Snippet**: ```text matplotlib numpy pandas ``` The installation documentation also uses unconstrained dependencies: ```text pip install pandas matplotlib numpy scipy ``` ```text pip install -r requirements.txt ``` These commands appear at `SKILL.md:111` and `SKILL.md:229`, respectively. The first command also introduces `scipy`, which is absent from `requirements.txt` and unused by the reviewed implementation. ### Technical Analysis The project specifies neither exact dependency versions nor cryptographic hashes. Consequently, each installation resolves whatever distributions the configured package index currently considers suitable. This makes installations non-reproducible and prevents verification that downloaded artifacts are the versions reviewed and approved by the project. The issue is a supply-chain weakness rather than evidence that the currently named packages are malicious. Exploitation requires compromise of a configured package source, package or maintainer account, dependency-resolution environment, or another mechanism that causes pip to select an attacker-controlled artifact. ### Attack Path 1. An operator follows the installation instructions in `SKILL.md`. 2. `pip` resolves the unconstrained package names using the operator's configured indexes. 3. An attacker compromises an upstream release channel or otherwise causes a malicious distribution to be selected. 4. The malicious package executes installation-time behavior, or malicious code runs when `scripts/main.py` imports the package. 5. That code executes with the privileges of the user or automation account running the installation or skill. ### Impact Assessment Successful exploitation could provide arbitrary code execution under the installing or ru ...[truncated 428 chars]
- Remediation
- ## Remediation Suggestions 1. Pin every direct dependency to an explicitly reviewed version. 2. Generate a lock file containing all transitive dependencies and cryptographic hashes. 3. Install with hash enforcement, such as `pip install --require-hashes -r requirements.txt`. 4. Use a trusted, explicitly configured package index or an internally controlled artifact repository. 5. Add automated dependency vulnerability and integrity scanning to the release process. 6. Update dependencies through a controlled review process rather than resolving new releases during routine installation. 7. Remove `scipy` from `SKILL.md` unless it becomes a documented and reviewed runtime dependency. 8. Keep installation instructions synchronized with the authoritative locked dependency manifest.