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Security audit

Shopify 电商视频生成与编辑

Security checks for vulnerabilities and agentic risk

Overview

This skill is a disclosed AI Hive video-generation tool that sends user-selected prompts and media to AI Hive and stores an API key locally, with setup/privacy cautions but no evidence of hidden or harmful behavior.

Install in a virtual environment, consider pinning requests before use, and only provide AI Hive credentials and product media you are comfortable sending to the AI Hive service. Review pricing before submitting video jobs because generation may incur account charges.

Vulnerability Patterns
  • Insecure DependenciesIntroduces malicious components through unsafe dependency sources
  • 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
Findings (1)

T08 · Insecure Dependencies

Warning
Location
SKILL.md:78
Finding

Unpinned Third-Party Dependency Installation

Content
View full analysis
``` ``` `scripts/videogen.py:17-22`: ```python try: import requests except ImportError: print("缺少依赖:requests。请运行 pip3 install requests", file=sys.stderr) sys.exit(1) ``` ### Technical Analysis The documented setup command installs `requests` without a pinned version, cryptographic hashes, a lockfile, or an explicitly trusted package index. The actual package and transitive dependency versions installed therefore depend on the state and configuration of the user's Python package environment at installation time. Although `requests` is a legitimate and correctly spelled package, this installation process is not reproducible. If the configured package index, package account, or dependency distribution is compromised, the user could install code that was not present during this Skill audit. The script subsequently imports the installed package, allowing malicious module-level code to execute. Exploitation is contingent on compromise or substitution of the package source, package release, or local pip configuration; the audited repository itself does not contain a malicious dependency payload. ### Attack Path 1. An attacker compromises a relevant package distribution or controls the package index selected by the user's pip configuration. 2. The attacker publishes or serves a malicious version of `requests` or one of its resolved dependencies. 3. The user follows the Skill documentation and runs `pip3 install requests`. 4. Because no version or hashes are enforced, pip accep ...[truncated 1012 chars]
Remediation
View remediation
\ --hash=sha256: ``` 2. Pin and hash all transitive dependencies, not only the top-level package. Generate the lock file in a controlled environment using a dependency-locking tool. 3. Install dependencies with hash enforcement: ```bash python3 -m pip install --require-hashes -r requirements.txt ``` 4. Use an explicitly trusted package index rather than relying on ambient pip configuration: ```bash python3 -m pip install \ --index-url https://pypi.org/simple \ --require-hashes \ -r requirements.txt ``` 5. Review and update pinned versions through a controlled process that includes vulnerability scanning, integrity verification, and testing. 6. Recommend installation inside a dedicated virtual environment and explicitly warn users not to run installation commands as root or with administrator privileges. ]]>
Vulnerability Patterns
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • 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)

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
94% confidence
Finding

This markdown file contains user-facing natural-language content exclusively in Chinese, which can amount to a language/locale policy violation when no opt-in, alternative language, or justification is provided. Under SQP-3, forced use of a specific language without user choice should be flagged.

Content

No source excerpt is available for this finding.

Undeclared Tool Scope

Medium
Category
MCP Least Privilege
Confidence
92% confidence
Finding

The skill invokes a local Python script with installation and runtime commands that imply shell, network, file read/write, and possible environment access, but it does not declare any explicit tool scope or permissions boundaries. In an agent environment, this increases the risk of over-broad execution and unintended access because the agent may run commands or handle files/network operations without a clearly constrained policy.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
91% confidence
Finding

This markdown file describes when to use the skill with a long, open-ended list of ecommerce video scenarios, but it does not define explicit trigger phrases, limits, or negative examples. That ambiguity can cause unintended invocation for loosely related video-editing requests, especially because the scope spans many common marketing assets.

Content

No source excerpt is available for this finding.

Description-Behavior Mismatch

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The file contains a broad general-purpose AI CLI framework while the skill is presented as a Shopify video-generation/editing skill. This scope mismatch increases attack surface by exposing capabilities for text chat, image generation, model discovery, uploads, and account operations that are unnecessary for the declared purpose and could be invoked by an agent or user outside the expected workflow.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The module docstring is written entirely in Chinese and presents the tool as a general-purpose skill, while later CLI help, status messages, prompts, and errors also assume Chinese-only interaction. For a general skill, this is a natural-language locale constraint that is not presented as optional or justified as region-specific.

Content

No source excerpt is available for this finding.

Context-Inappropriate Capability

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

Exposing user-info and wallet-balance retrieval is outside the declared Shopify video-editing function and reveals account metadata that may be sensitive in multi-tenant or agent-driven environments. Even if the data is only returned to the caller, unnecessary account inspection capabilities violate least privilege and can aid reconnaissance or billing abuse awareness.

Content

No source excerpt is available for this finding.

Context-Inappropriate Capability

Medium
Category
Not specified by scanner
Confidence
94% confidence
Finding

The standalone text-chat operation enables a generic LLM capability unrelated to the skill's declared video purpose. In an agent ecosystem, this can be repurposed for arbitrary external prompting and data transmission, expanding both misuse potential and the channel through which sensitive inputs may be sent to the remote API.

Content

No source excerpt is available for this finding.

Context-Inappropriate Capability

Medium
Category
Not specified by scanner
Confidence
92% confidence
Finding

The standalone image-generation path broadens the tool beyond video creation/editing and allows independent remote content generation not described by the manifest. This unnecessary capability expansion increases the chance of policy bypass, unexpected spend, and unintended data sharing through uploaded reference images or prompts.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Low
Category
Not specified by scanner
Confidence
95% confidence
Finding

The skill description includes Chinese text alongside English, which implies a language/locale behavior but does not state whether multilingual output is optional or user-selected. Under the policy, forcing or assuming a language without opt-in can be a natural-language policy issue.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.