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Security audit

Alibaba Cloud Data Lake DLF Next

Security checks for vulnerabilities and agentic risk

Overview

This skill is a disclosed Alibaba Cloud DLF Next helper that uses expected cloud credentials, Aliyun API metadata, and local output files without evidence of hidden or unrelated behavior.

Install this only if you want an agent to operate against Alibaba Cloud DLF Next. Use least-privilege, preferably short-lived credentials, confirm the region and resource identifiers before any mutation, and review files written under output/alicloud-data-lake-dlf-next/.

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
  • 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
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
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
97% confidence
Finding
The declared purpose says the skill manages DlfNext resources, but the described behavior focuses on metadata discovery and local artifact generation rather than actual DlfNext resource operations. This mismatch is dangerous because users and automated policy systems may grant trust, credentials, or execution rights based on the stated purpose while the skill performs materially different actions.

Lp3

Medium
Category
MCP Least Privilege
Confidence
90% confidence
Finding
The skill advertises executable behavior involving environment access, file writes, and network use, but does not declare any explicit tool scope such as allowed tools or permissions. This weakens containment and review because consumers cannot easily verify what capabilities the skill expects, increasing the risk of unintended secret access, outbound requests, or filesystem modification.

Natural-Language Policy Violations

Medium
Confidence
87% confidence
Finding
Allowing the skill to choose 'the most reasonable region' when no region is specified can cause operations or metadata access to occur in an unintended jurisdiction or account context. For cloud governance services, region selection can affect compliance, data residency, cost, and whether actions target the correct resources.

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
79% confidence
Finding
The skill instructs the use of Alibaba Cloud credentials from environment variables and shared credential files but does not include explicit handling guidance, masking requirements, or warnings against logging/exfiltration. In a network-enabled skill, omission of secret-safety guidance raises the chance that sensitive credentials are exposed through debugging, artifact writes, or misuse.

Static analysis

No suspicious patterns detected.