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Security audit

Alibaba Cloud Database Analyticdb Mysql

Security checks for vulnerabilities and agentic risk

Overview

This skill is a disclosed Alibaba Cloud AnalyticDB management helper with expected credential, network, and local-output behavior, but users should treat cloud mutations as potentially billable or disruptive.

Install only if you intend to let the agent help with Alibaba Cloud AnalyticDB for MySQL tasks. Use least-privilege Alibaba Cloud credentials, review any create/update/modify/set call before it runs, and keep generated API metadata under the documented output directory.

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
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • Taint TrackingDirect Taint Flow, Variable-Mediated Taint Flow, Credential Exfiltration Chain
  • MCP Least PrivilegeUnderdeclared Capability, Wildcard Permission, Missing Permission Declaration
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
Findings (9)

Tainted flow: 'timeout' from os.getenv (line 34, credential/environment) → urllib.request.urlopen (network output)

Critical
Category
Data Flow
Content
def fetch_json(url: str, timeout: int) -> dict:
    req = urllib.request.Request(url, headers={"User-Agent": "codex-skill"})
    with urllib.request.urlopen(req, timeout=timeout) as resp:
        return json.loads(resp.read().decode("utf-8"))
Confidence
90% confidence
Finding
Credentials or environment variables flow to a network sink. This is a high-confidence indicator of credential exfiltration.

Tp4

High
Category
MCP Tool Poisoning
Confidence
96% confidence
Finding
The declared purpose is ADB resource management, but the described behavior is metadata discovery, artifact generation, and support for arbitrary product codes and versions. This mismatch is dangerous because operators may grant trust or cloud-related permissions assuming a narrow ADB-management scope, while the actual behavior is broader and can be repurposed for unintended network access and filesystem writes.

Lp3

Medium
Category
MCP Least Privilege
Confidence
92% confidence
Finding
The skill appears to use environment variables, local file writes, and network access, but it does not declare any explicit tool scope or permissions boundary. In an agent setting, this weakens reviewability and can allow broader-than-expected execution, especially because the skill handles cloud credentials and instructs network-based API discovery.

Vague Triggers

Medium
Confidence
95% confidence
Finding
The description says to use the skill for managing AnalyticDB for MySQL, including lifecycle operations, configuration, status checks, and troubleshooting, but it does not define concrete trigger phrases, scope limits, or exclusion examples. This broad wording could overlap with many generic cloud-management requests and may cause unintended invocation.

Missing User Warnings

Medium
Confidence
89% confidence
Finding
The skill discusses create, update, modify, and set operations against cloud resources without a clear user-facing warning or confirmation gate for destructive or costly changes. In a cloud-management context, ambiguous mutation guidance can lead to accidental resource changes, downtime, configuration drift, or unnecessary charges when the skill is invoked too broadly.

External Transmission

Medium
Category
Data Exfiltration
Content
output_dir.mkdir(parents=True, exist_ok=True)

    url = (
        f"https://api.aliyun.com/meta/v1/products/{args.product_code}"
        f"/versions/{args.version}/api-docs.json"
    )
    payload = fetch_json(url, timeout)
Confidence
60% confidence
Finding
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

External Transmission

Medium
Category
Data Exfiltration
Content
output_dir.mkdir(parents=True, exist_ok=True)

    url = (
        f"https://api.aliyun.com/meta/v1/products/{args.product_code}"
        f"/versions/{args.version}/api-docs.json"
    )
    payload = fetch_json(url, timeout)
Confidence
60% confidence
Finding
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

External Transmission

Medium
Category
Data Exfiltration
Content
output_dir.mkdir(parents=True, exist_ok=True)

    url = (
        f"https://api.aliyun.com/meta/v1/products/{args.product_code}"
        f"/versions/{args.version}/api-docs.json"
    )
    payload = fetch_json(url, timeout)
Confidence
60% confidence
Finding
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

External Transmission

Medium
Category
Data Exfiltration
Content
output_dir.mkdir(parents=True, exist_ok=True)

    url = (
        f"https://api.aliyun.com/meta/v1/products/{args.product_code}"
        f"/versions/{args.version}/api-docs.json"
    )
    payload = fetch_json(url, timeout)
Confidence
60% confidence
Finding
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.

Static analysis

No suspicious patterns detected.