T08 · Insecure Dependencies
- Location
README.md:25- Finding
Unpinned Third-Party Dependencies Installed Without Integrity Verification
- Content
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Vulnerability Details
File Location:
README.md:25; also present atQUICK_START.md:92
Vulnerability Type: Unpinned and integrity-unverified package installation
Risk Level: LowVulnerable Code
README.md:25:bash pip install pandas openpyxlQUICK_START.md:92:bash pip install pandas numpyTechnical Analysis
The installation instructions request packages by name without specifying exact versions or cryptographic hashes. The project also contains no dependency lock file or hash-verified requirements file. Consequently, the installed code depends on whichever package versions the configured Python package index resolves at installation time.
This does not demonstrate that the named packages are currently malicious. However, it creates a supply-chain weakness because installation results are not reproducible and do not enforce the versions reviewed or tested by the project author. A compromised upstream publisher account, package index, mirror, or future malicious release could cause different code to be installed under the same documented command.
The analyzer imports
pandasduring startup. Therefore, code introduced through a compromised dependency could execute when Python imports the installed package, even if the skill's own source remains unchanged.Attack Path
- An operator follows the documented
pip installcommand. pipresolves the latest compatible package versions from the operator's configured index or mirror.- An attacker who has compromised an upstream release process, publisher account, index, or mirror supplies a malicious package version.
- Because no version or hash is enforced, the malicious artifact is accepted as satisfying the installation command.
- The operator invokes the lottery analyzer.
- The analyzer imports the affected dependency, allowing its Python or native initialization code to run with the privileges of the invok ...[truncated 785 chars]
- An operator follows the documented
- Remediation
View remediation
Remediation Suggestions
-
Create a reviewed requirements file with exact versions, for example:
text pandas==<reviewed-version> numpy==<reviewed-version> openpyxl==<reviewed-version> -
Generate and verify hashes for every direct and transitive dependency. Install with a command such as:
bash python -m pip install --require-hashes -r requirements.txt -
Use a lock-file tool such as
pip-tools, Poetry, or uv to produce reproducible dependency resolution. -
Pin transitive dependencies as well as direct dependencies and review lock-file changes during upgrades.
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Use an approved package index or internally controlled mirror, and require TLS certificate validation.
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Add automated dependency vulnerability and provenance scanning to the release process.
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Replace the unpinned commands in both
README.mdandQUICK_START.mdwith installation through the reviewed lock or requirements file.
-