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Security audit

Seedream 5.0 Lite 产品图生成

Security checks for vulnerabilities and agentic risk

Overview

This is a coherent AI Hive product-image generation skill with some install and credential-handling cautions but no artifact-backed malicious behavior.

Install this only if you are comfortable sending selected product/reference images and prompts to AI Hive and storing an AI Hive API key locally. Prefer using a virtual environment and pinning requests before installation; review the bundled script if your environment restricts broad helper code even when it is not exposed by the active skill entrypoint.

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:69
Finding

Unpinned Third-Party Dependency Installation

Content
View full analysis

Vulnerability Details

File Location: SKILL.md, line 69
Vulnerability Type: Unpinned third-party dependency
Risk Level: Medium

bash
pip3 install requests
python3 "$SKILL_PATH/scripts/imagegen.py" init --skill-name seedream-5-lite-product-image
python3 "$SKILL_PATH/scripts/imagegen.py" task --task-id <taskId>

Technical Analysis

The installation instructions direct users to install requests without specifying a reviewed version or verifying the package artifact with a cryptographic hash. Consequently, dependency resolution can change over time and will select whichever compatible release is available from the configured package index at installation time.

If the upstream package, package-index account, configured index, or dependency delivery channel is compromised, a malicious or altered release could be installed. The project provides no lock file, version constraint, or hash allowlist to prevent such an artifact from being accepted. The resulting environment is also not reproducible, which makes security review and incident response more difficult.

Attack Path

  1. An attacker compromises the relevant package publication account, package index, configured mirror, or artifact delivery channel.
  2. The attacker makes a malicious package release or altered artifact available to dependency resolution.
  3. A user follows the documented pip3 install requests instruction.
  4. pip3 resolves and installs the attacker-controlled artifact because no version or hash is enforced.
  5. Malicious code executes during package use by scripts/imagegen.py, with the privileges of the user running the Skill.

Impact Assessment

Successful exploitation could execute arbitrary Python code in the Skill user's context. This may expose the AI Hive API key, user-selected media, generated output, and other files accessible to that account. It could also modify the Python environment or make unauthorized ...[truncated 292 chars]

Remediation
View remediation

Remediation Suggestions

  1. Replace the unconstrained installation command with a reviewed, exact version pin, such as requests==X.Y.Z.
  2. Maintain dependencies in a lock or requirements file containing cryptographic hashes.
  3. Install with hash enforcement, for example:
    bash
    python3 -m pip install --require-hashes -r requirements.txt
    
  4. Generate the lock file from a trusted package index and review dependency updates before changing pinned versions or hashes.
  5. Recommend installation inside a dedicated virtual environment rather than a system-wide Python environment.
  6. Use a trusted package index over TLS and avoid unreviewed mirrors or additional indexes.
  7. Add automated dependency and vulnerability scanning to the release process.
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 (10)

Tp4

High
Category
MCP Tool Poisoning
Confidence
95% confidence
Finding

The declared purpose is narrow product-image generation, but the underlying implementation reportedly exposes broader capabilities such as chat, video generation, account/balance queries, model enumeration, uploads, and task querying. This mismatch can mislead users and reviewers about what the skill can actually do, enabling capability creep, unauthorized data handling, or abuse of higher-privilege APIs under an innocuous description.

Content

No source excerpt is available for this finding.

Context-Inappropriate Capability

High
Category
Not specified by scanner
Confidence
97% confidence
Finding

The presence of generic chat and video-generation functions materially exceeds the advertised purpose of a fixed product-image generator. In a skill ecosystem, this can enable unauthorized prompt forwarding, broader content generation, and policy bypass if callers rely on metadata rather than code review to understand what the skill can do.

Content

No source excerpt is available for this finding.

Undeclared Tool Scope

Medium
Category
MCP Least Privilege
Confidence
91% confidence
Finding

The skill includes executable shell commands and an external Python script invocation with package installation, but it declares no explicit tool scope or permission boundaries. That creates an unnecessary trust gap: an agent may grant broader shell, file, environment, and network access than users expect for an image-generation skill, increasing the chance of unintended code execution or data exposure.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
92% confidence
Finding

The manifest description lists broad invocation phrases such as "商品主图", "白底图", "材质细节", and "新品上架;通过 AI Hive 生成" without defining specific trigger syntax, scope limits, or exclusion conditions. In a markdown/manifest context, this can cause unintended activation because these phrases overlap with common ecommerce and image-editing requests.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
88% confidence
Finding

The natural-language description and operating instructions are written as if the skill should be used in Chinese (for example, the invocation examples and guidance are entirely Chinese), but there is no statement that language choice is optional or that the skill is region-specific. This can violate language/locale policy when a skill implicitly forces a specific language without user opt-in.

Content

No source excerpt is available for this finding.

Description-Behavior Mismatch

Medium
Category
Not specified by scanner
Confidence
98% confidence
Finding

The file is presented as a fixed Seedream 5.0 Lite product-image skill, but its implementation is a generic AI Hive client supporting unrelated capabilities such as chat, video generation, model enumeration, uploads, and account queries. This scope mismatch is dangerous because downstream systems or reviewers may grant permissions and trust based on the narrower declared purpose, while the code can perform broader actions than expected.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
98% confidence
Finding

The module docstring presents the skill description entirely in Chinese, and this pattern continues throughout user-facing prompts and CLI help text. This can violate a language/locale policy when the skill forces a specific language experience without giving the user any explicit choice or opt-in.

Content

No source excerpt is available for this finding.

Context-Inappropriate Capability

Medium
Category
Not specified by scanner
Confidence
91% confidence
Finding

Exposing user-info and model-listing operations in a skill that claims to only generate product images violates least privilege and increases the available attack surface. An invoking agent or user expecting only image generation could unintentionally disclose account metadata, wallet details, or available model inventory through this broader interface.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The embedded metadata claims the skill is fixed to Seedream 5.0 Lite image generation, while the file-level implementation and documentation describe a generic multi-capability tool. Misleading metadata is dangerous because governance, approval, and orchestration layers often depend on declared scope; this discrepancy can conceal broader functionality from operators and automated controls.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Low
Category
Not specified by scanner
Confidence
88% confidence
Finding

This markdown file contains user-facing natural language almost exclusively in Chinese, with no indication that the skill is region-specific or that other language options are available. Under the policy rule for language/locale constraints, forcing a specific language without opt-in can be a natural-language policy violation.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.