Security Audit

analyzing-time-series

github.com/https-deeplearning-ai/sc-agent-skills-files

AI SkillCommit d3e7b4f6ec5d

CAUTION

Scanned 9 days ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

analyzing-time-series received a trust score of 67/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 3 findings: 0 critical, 2 high, 0 medium, and 1 low severity. Key findings include Arbitrary File Read via Unsanitized File Path, Path Traversal Allows Arbitrary File Write, Unpinned Dependencies.

The analysis covered 4 layers: manifest_analysis, llm_behavioral_safety, dependency_graph, static_code_analysis. The static_code_analysis layer scored lowest at 68/100, indicating areas for improvement.

Last analyzed on February 11, 2026 (commit d3e7b4f6). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis

100%

Static Code Analysis

68%

Dependency Graph

100%

LLM Behavioral Safety

100%

Behavioral Risk Signals

Filesystem Write

3 findings

Dynamic Code

1 finding

Security Findings3

Severity	Finding	Layer	Location
HIGH	Arbitrary File Read via Unsanitized File Path The `load_data` function in `scripts/ts_utils.py`, used by both `diagnose.py` and `visualize.py`, passes a user-controllable file path directly to `pd.read_csv()`. An attacker could craft a prompt instructing the agent to use a path to a sensitive file on the system (e.g., `/etc/passwd`, `~/.aws/credentials`) instead of the expected CSV data file. This would cause the script to read and potentially expose the contents of an arbitrary file. Sanitize and validate the input file path. Ensure that the path is resolved to a canonical path and is confirmed to be within a designated, safe working directory (e.g., a temporary workspace for user uploads). Disallow absolute paths and path traversal sequences like `../`.	Unknown	scripts/ts_utils.py:29
HIGH	Path Traversal Allows Arbitrary File Write The scripts `diagnose.py` and `visualize.py` accept an `--output-dir` argument to specify where to save result files. The scripts do not validate or sanitize this path, allowing for path traversal attacks. An attacker could provide a malicious path like `../../../../tmp` to write files (`diagnostics.json`, various `.png` plots) outside the intended project directory. This could lead to denial of service by filling up the disk, or overwriting system or user files. Sanitize the `--output-dir` path. Resolve the path to its absolute form and verify that it is a subdirectory of a designated, safe working directory. Disallow absolute paths and path traversal sequences.	Unknown	scripts/visualize.py:49
LOW	Unpinned Dependencies The `SKILL.md` file lists project dependencies (`pandas`, `numpy`, etc.) without specifying versions. If the agent's environment is built dynamically based on this information, it could lead to installing a newer, potentially breaking version or, in a compromised scenario, a malicious version of a dependency. This introduces a supply chain risk. Provide a `requirements.txt` or similar dependency manifest file with pinned versions for all dependencies (e.g., `pandas==2.2.0`). For stronger integrity guarantees, use a hash-checking mechanism like `pip install -r requirements.txt --require-hashes`.	Unknown	SKILL.md:69

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