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Security audit

Alibaba Cloud AI Chatbot

Security checks for vulnerabilities and agentic risk

Overview

This is a coherent Alibaba Cloud chatbot management skill with expected credential and API use, but users should be careful with live cloud changes.

Install this only if you want the agent to help manage Alibaba Cloud Chatbot resources. Use least-privilege Alibaba credentials, confirm the exact region, resource ID, and intended change before any mutation, and keep secrets out of saved output artifacts.

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
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Findings (11)

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.

Lp3

Medium
Category
MCP Least Privilege
Confidence
95% confidence
Finding
The skill advertises operational behavior that can access environment credentials, write files, and make networked API calls, but it does not declare any explicit tool or permission scope. This creates an authorization ambiguity where an agent may invoke sensitive capabilities without clear guardrails, increasing the risk of unintended cloud actions or secret exposure during execution.

Vague Triggers

Medium
Confidence
90% confidence
Finding
The invocation description is broad enough to match many generic chatbot configuration or troubleshooting requests, which can cause over-selection of this skill in situations where cloud-management actions were not intended. In the context of a cloud administration skill, accidental activation is more dangerous because it may lead to credential use, API enumeration, or real configuration changes.

Missing User Warnings

Medium
Confidence
93% confidence
Finding
The workflow explicitly includes create, update, modify, and set operations but does not require a clear warning that these actions can change production chatbot resources. In a cloud-control context, this omission increases the chance of destructive or unintended changes being executed under ambiguous user requests.

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.

Missing User Warnings

Low
Confidence
88% confidence
Finding
The skill instructs use of environment variables and shared credential files but does not warn against printing, persisting, or including secrets in outputs and logs. Because the skill also directs saving artifacts under an output directory, there is a realistic risk of accidental credential leakage into evidence files or command transcripts.

Natural-Language Policy Violations

Low
Confidence
82% confidence
Finding
Telling the agent to choose 'the most reasonable region' when no region is set allows it to make a deployment-locale decision without explicit user approval. In cloud environments, region choice can affect data residency, billing, and which resources are modified, so even a seemingly small assumption can lead to unintended impact.

Missing User Warnings

Low
Confidence
78% confidence
Finding
This code creates the output directory and writes both a JSON file and a Markdown file, but the only user-facing disclosure appears after the writes complete. Under the code-file warning criteria, file writes should have some visible disclosure, confirmation, or explanatory comment/docstring warning the user about the action before it occurs.

Static analysis

No suspicious patterns detected.