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Security audit

AWS Health Monitor

Security checks for vulnerabilities and agentic risk

Overview

This skill does what it claims: it polls the public AWS Health feed and sends configured outage notifications, with disclosed local state and optional cron use.

Before installing, confirm you want a scheduled monitor that can make outbound requests to AWS Health and send outage messages to your configured notification target. Set `AWS_HEALTH_NOTIFY_TARGET` carefully, use region/service filters if you do not want broad monitoring, and only add the cron entry if continuous polling is desired.

A.I.G

Vulnerability Patterns
  • Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
  • Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
  • Remote Payload Retrieval and ExecutionFetches external code whose behavior can change after review
  • Embedded Malicious CodeShips malicious scripts inside the skill and executes them locally
  • Unauthorized Access and Privilege EscalationObtains permissions beyond the task's legitimate needs

SkillSpector

Vulnerability Patterns
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • Behavioral ASTexec() Call, eval() Call, Dynamic Import
  • Taint TrackingDirect Taint Flow, Variable-Mediated Taint Flow, Credential Exfiltration Chain
  • MCP Least PrivilegeUnderdeclared Capability, Wildcard Permission, Missing Permission Declaration
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Findings (7)

Undeclared Tool Scope

Medium
Category
MCP Least Privilege
Confidence
91% confidence
Finding

The skill documents capabilities that involve environment variables, filesystem writes, network access, and shell/Cron execution, but it does not declare any explicit tool scope or permission boundaries. This creates a real security issue because an agent may invoke the skill with broader-than-expected privileges, increasing the chance of unauthorized file modification, network egress, or secret exposure via environment variables and logs.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
94% confidence
Finding

The description says to activate when the user 'wants to watch AWS service health, set up alerts for AWS outages, or check current AWS regional incidents,' which is a broad natural-language condition rather than a specific invocation scope. It does not provide explicit trigger phrases, exclusions, or negative examples, so it could overlap with general AWS troubleshooting requests and cause unintended invocation.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
82% confidence
Finding

The skill documentation and examples are predominantly in Chinese, including labels and notification examples, without indicating that language is user-selectable or optional. This can violate language/locale policy if the skill implicitly constrains output language regardless of user preference.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
97% confidence
Finding

The file's top-level description and inline user-facing text are written entirely in Chinese, indicating the skill is intended to operate in a fixed language. There is no natural-language indication that users may opt into another language or that the locale restriction is justified as region-specific, which matches the language/locale policy violation criteria.

Content

No source excerpt is available for this finding.

subprocess module call

Medium
Category
Dangerous Code Execution
Confidence
70% confidence
Finding

subprocess module calls execute external commands. Without careful input validation, this enables command injection.

Content

Scanner excerpt · scripts/aws-health-monitor.py (reported line 273)May include surrounding context.

python
now = datetime.now(CST).strftime("%Y-%m-%d %H:%M:%S")
    if notifications:
        for msg in notifications:
            result = subprocess.run(
                ["openclaw", "message", "send",
                 "--channel", NOTIFY_CHANNEL,
                 "--target", NOTIFY_TARGET,

Tainted flow: 'NOTIFY_CHANNEL' from os.environ.get (line 27, credential/environment) → subprocess.run (code execution)

Medium
Category
Data Flow
Confidence
65% confidence
Finding

Data from a source is assigned to a variable that is later passed to a sink, creating a variable-mediated taint flow.

Content

Scanner excerpt · scripts/aws-health-monitor.py (reported line 273)May include surrounding context.

python
now = datetime.now(CST).strftime("%Y-%m-%d %H:%M:%S")
    if notifications:
        for msg in notifications:
            result = subprocess.run(
                ["openclaw", "message", "send",
                 "--channel", NOTIFY_CHANNEL,
                 "--target", NOTIFY_TARGET,

Natural-Language Policy Violations

Low
Category
Not specified by scanner
Confidence
94% confidence
Finding

The only natural-language guidance in this file is written in Chinese, which imposes a specific language on users without any opt-in or indication that the file is intended solely for a Chinese-speaking or region-specific audience. Under the policy, language-specific instructions should either provide a choice or clearly justify the locale restriction.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.