Trust Assessment
memory-sync received a trust score of 10/100, placing it in the Untrusted category. This skill has significant security findings that require attention before use in production.
SkillShield's automated analysis identified 9 findings: 4 critical, 4 high, 0 medium, and 0 low severity. Key findings include Unsafe environment variable passthrough, Arbitrary command execution, Credential harvesting.
The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The Manifest Analysis layer scored lowest at 0/100, indicating areas for improvement.
Last analyzed on February 14, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
Security Findings9
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| CRITICAL | Arbitrary command execution Python shell execution (os.system, subprocess) Review all shell execution calls. Ensure commands are static (not built from user input), use absolute paths, and are strictly necessary. Prefer library APIs over shell commands. | Manifest | skills/mpesavento/memory-sync/memory_sync.py:1585 | |
| CRITICAL | Credential harvesting Reading well-known credential environment variables Skills should only access environment variables they explicitly need. Bulk environment dumps (os.environ.copy, JSON.stringify(process.env)) are almost always malicious. Remove access to Keychain, GPG keys, and credential stores. | Manifest | skills/mpesavento/memory-sync/memory_sync.py:1626 | |
| CRITICAL | Credential harvesting Reading well-known credential environment variables Skills should only access environment variables they explicitly need. Bulk environment dumps (os.environ.copy, JSON.stringify(process.env)) are almost always malicious. Remove access to Keychain, GPG keys, and credential stores. | Manifest | skills/mpesavento/memory-sync/memory_sync.py:1635 | |
| CRITICAL | Credential harvesting Reading well-known credential environment variables Skills should only access environment variables they explicitly need. Bulk environment dumps (os.environ.copy, JSON.stringify(process.env)) are almost always malicious. Remove access to Keychain, GPG keys, and credential stores. | Manifest | skills/mpesavento/memory-sync/memory_sync.py:1701 | |
| HIGH | Unsafe environment variable passthrough Access to well-known credential environment variables Minimize environment variable exposure. Only pass required, non-sensitive variables to MCP servers. Use dedicated secret management instead of environment passthrough. | Manifest | skills/mpesavento/memory-sync/memory_sync.py:1626 | |
| HIGH | Unsafe environment variable passthrough Access to well-known credential environment variables Minimize environment variable exposure. Only pass required, non-sensitive variables to MCP servers. Use dedicated secret management instead of environment passthrough. | Manifest | skills/mpesavento/memory-sync/memory_sync.py:1635 | |
| HIGH | Unsafe environment variable passthrough Access to well-known credential environment variables Minimize environment variable exposure. Only pass required, non-sensitive variables to MCP servers. Use dedicated secret management instead of environment passthrough. | Manifest | skills/mpesavento/memory-sync/memory_sync.py:1701 | |
| HIGH | Dangerous call: subprocess.run() Call to 'subprocess.run()' detected in function 'summarize_with_openclaw'. This can execute arbitrary code. Avoid using dangerous functions like exec/eval/os.system. Use safer alternatives. | Static | skills/mpesavento/memory-sync/memory_sync.py:1585 | |
| INFO | Unpinned dependencies in installation instructions The installation instructions recommend installing 'click' and 'openai' without specifying version pins. This can lead to non-reproducible builds and potential security vulnerabilities if a future version of a dependency introduces breaking changes or security flaws. While common for simple scripts, it's best practice to pin dependencies for reproducibility and to mitigate supply chain risks. Specify exact or range-based versions for dependencies in installation instructions (e.g., `pip install click==8.1.3` or `pip install openai>=1.0,<2.0`). For more complex projects, consider using a `requirements.txt` or `pyproject.toml` file with pinned versions. | LLM | SKILL.md:15 |
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