Trust Assessment
camsnap received a trust score of 82/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.
SkillShield's automated analysis identified 2 findings: 0 critical, 1 high, 1 medium, and 0 low severity. Key findings include Potential Command Injection via '--action' parameter, Credential Exposure via Command Line Arguments.
The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. All layers scored 70 or above, reflecting consistent security practices.
Last analyzed on February 18, 2026 (commit b62bd290). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
Security Findings2
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| HIGH | Potential Command Injection via '--action' parameter The skill documentation for `camsnap watch` explicitly shows the use of an `--action` parameter, which allows executing arbitrary shell commands upon motion detection. If the AI agent constructs this command using untrusted user input for the `--action` argument, it could lead to arbitrary command execution on the host system. This is a direct command injection vulnerability inherent in the tool's design and exposed by the skill's usage examples. When constructing `camsnap watch` commands, ensure that the `--action` parameter's value is either hardcoded, strictly validated against a whitelist of safe commands, or completely sanitized to prevent injection of malicious shell commands. Avoid directly embedding untrusted user input into this argument. | LLM | SKILL.md:17 | |
| MEDIUM | Credential Exposure via Command Line Arguments The `camsnap add` command example demonstrates passing sensitive credentials (`--user` and `--pass`) directly as command-line arguments. While this is a feature of the `camsnap` tool, it poses a security risk. Credentials passed this way can be exposed in process lists (e.g., `ps aux`), shell history, or system logs, making them vulnerable to unauthorized access. If the AI agent is prompted to use user-provided credentials for this command, it could inadvertently expose them. Advise users to use more secure methods for credential management, such as environment variables, configuration files with restricted permissions, or a secure credential store, if `camsnap` supports them. If command-line arguments are unavoidable, ensure the AI agent is designed to never log these commands and to handle user-provided credentials with extreme care, potentially masking them or using temporary, short-lived tokens where possible. | LLM | SKILL.md:10 |
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