Security Audit
passslot-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
passslot-automation received a trust score of 86/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.
SkillShield's automated analysis identified 1 finding: 0 critical, 1 high, 0 medium, and 0 low severity. Key findings include Excessive Permissions via Broad Tool Access.
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 17, 2026 (commit 99e2a295). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| HIGH | Excessive Permissions via Broad Tool Access The skill instructs the LLM to use highly privileged Rube MCP tools such as `RUBE_MULTI_EXECUTE_TOOL` and `RUBE_REMOTE_WORKBENCH`. `RUBE_MULTI_EXECUTE_TOOL` allows the LLM to perform any operation exposed by the Passslot toolkit, while `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` implies the ability to execute arbitrary Composio tools. This broad access, if exploited by a malicious prompt, could lead to unauthorized data modification, deletion, or creation within the connected Passslot account or other Composio-integrated services. The skill's design grants the LLM significant power over the connected Passslot account. Consider if the skill truly requires such broad access. If specific Passslot operations are intended, narrow the scope of the skill to only expose those specific operations, rather than a generic multi-execute tool. Implement granular access controls within the Rube MCP or Passslot toolkit to restrict the types of actions an LLM can perform. Ensure robust input validation and prompt injection defenses are in place for the LLM interacting with this skill, as a compromised LLM could leverage these broad permissions. | LLM | SKILL.md:49 |
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