Security Audit

jira

github.com/openclaw/skills

AI SkillCommit 13146e6a3d46

CRITICAL

Scanned 3 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

jira received a trust score of 49/100, placing it in the Untrusted category. This skill has significant security findings that require attention before use in production.

SkillShield's automated analysis identified 3 findings: 1 critical, 1 high, 1 medium, and 0 low severity. Key findings include Persistence / self-modification instructions, Persistence mechanism: Shell RC file modification, Potential Command Injection via CLI 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 13, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis

70%

Static Code Analysis

93%

Dependency Graph

100%

LLM Behavioral Safety

85%

Behavioral Risk Signals

Filesystem Write

2 findings

Shell Execution

3 findings

Dynamic Code

1 finding

Security Findings3

Severity	Finding	Layer	Location
CRITICAL	Persistence / self-modification instructions Shell RC file modification for persistence Remove any persistence mechanisms. Skills should not modify system startup configurations, crontabs, LaunchAgents, systemd services, or shell profiles.	Manifest	skills/jdrhyne/jira/SKILL.md:9
HIGH	Potential Command Injection via CLI Arguments The skill constructs and executes shell commands using the `jira` CLI tool. Several commands, such as `jira issue create`, `jira issue edit`, `jira issue comment add`, and `jira issue list -q` (JQL queries), take user-provided input as arguments (e.g., summary, description, JQL string). If the host LLM directly interpolates untrusted user input into these shell commands without proper sanitization or quoting, it could lead to command injection, allowing an attacker to execute arbitrary commands on the underlying system. The host LLM must ensure that all user-provided input used as arguments for shell commands is rigorously sanitized and properly quoted (e.g., using `shlex.quote()` in Python) before execution. This prevents malicious input from breaking out of the argument context and executing arbitrary commands.	LLM	SKILL.md:22
MEDIUM	Persistence mechanism: Shell RC file modification Detected Shell RC file modification pattern. Persistence mechanisms allow malware to survive system restarts. Review this persistence pattern. Skills should not modify system startup configuration.	Static	skills/jdrhyne/jira/SKILL.md:9

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