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Security audit

Amazon Review Reveyes

Security checks for vulnerabilities and agentic risk

Overview

This skill is a coherent Amazon-review fetching integration, with ordinary third-party API and credential risks that users should understand before installing.

Install only if you are comfortable sending ASINs, marketplace choices, and review-fetch requests to Reveyes and using API credits. Prefer a protected secret store or tightly permissioned config for REVEYES_API_KEY, and consider pinning the Reveyes SDK version in controlled environments.

Vulnerability Patterns
  • Insecure DependenciesIntroduces malicious components through unsafe dependency sources
  • Insecure Skill Coding PracticesFinds exploitable flaws such as hardcoded secrets or command injection
  • 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
Findings (2)

T08 · Insecure Dependencies

Warning
Location
clawhub.json:12
Finding

Unbounded Third-Party SDK Can Execute Unreviewed Supply-Chain Code

Content
View full analysis
=0.1.2"] ``` `README.md:24-26`: ```bash pip install reveyes ``` ### Technical Analysis The Skill installs the externally maintained `reveyes` Python package without an exact version pin or integrity hash. The `>=0.1.2` constraint permits future package versions that were not part of this audit, while the manual installation command resolves the latest version available from the configured Python package index. The package is imported and executed by `scripts/fetch.py`, and its `ReveyesClient` receives the `REVEYES_API_KEY`. The dependency also performs the Skill's network communication. Because its implementation is not included in the audited project, this repository cannot enforce which network destinations receive the credential or what additional local operations the package performs. No evidence establishes that the currently intended SDK version is malicious. The issue is the avoidable supply-chain exposure created by accepting arbitrary future releases. ### Attack Path 1. An attacker compromises the `reveyes` package publisher account, distribution infrastructure, or an accepted future release. 2. The user installs the Skill or follows `pip install reveyes`. 3. Dependency resolution selects the compromised version because no exact version or hash is required. 4. Python executes package-level code when `scripts/fetch.py` imports `reveyes`. 5. The compromised implementation receives the API key through `ReveyesClient(api_key=api_key)`. 6. Malicious package code can transmit the key or access resources available to the Agent's operating-system account. ### Impact Assessment Successful exploitation would execute Python code with the same privileges as the process running the Ski ...[truncated 342 chars]
Remediation
View remediation

T09 · Insecure Skill Coding Practices

Note
Location
README.md:32
Finding

Documentation Encourages Persistent Plaintext API-Key Storage

Content
View full analysis
Remediation
View remediation
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Rogue AgentSelf-Modification, Session Persistence
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • MCP Least PrivilegeUnderdeclared Capability, Wildcard Permission, Missing Permission Declaration
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Findings (8)

Session Persistence

Medium
Category
Rogue Agent
Confidence
90% confidence
Finding

Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.

Content

Scanner excerpt · README.md (reported line 39)May include surrounding context.

Get your key free at www.reveyes.cn → Dashboard → API Keys.

bash
# Add to ~/.zshrc or ~/.bashrc
export REVEYES_API_KEY="your_api_key_here"

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The README tells users to 'Just talk to your OpenClaw agent naturally' but all invocation examples are in Chinese, which implies a language-specific usage expectation without opt-in or justification. This is a natural-language policy concern because the skill documentation does not state whether other languages are supported or whether Chinese is required.

Content

No source excerpt is available for this finding.

Undeclared Tool Scope

Medium
Category
MCP Least Privilege
Confidence
91% confidence
Finding

The skill requires an API key and instructs execution of a local script, but it declares no explicit tool scope or allowed-tools boundary. That creates an authorization gap where an agent may invoke code or access secrets without a clearly documented permission model, increasing the chance of overbroad execution or secret exposure.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
90% confidence
Finding

The invocation criteria are broad enough to overlap with generic product-analysis or feedback-analysis requests, which can cause the skill to trigger when a user did not explicitly request external data retrieval. In context, that may lead to unintended third-party API calls, unnecessary data export, or use of paid credits without clear user intent.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The output section describes presenting and exporting review data but does not clearly warn that the data is obtained through an external third-party API. This reduces user transparency around outbound data access and export behavior, which can create privacy, compliance, and consent issues, especially in enterprise environments.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
94% confidence
Finding

This code includes user-facing error strings only in Chinese for multiple failure cases, which imposes a specific language on users without offering a choice. That is a natural-language policy concern because the file does not document that the skill is China-specific or otherwise limited to Chinese-speaking users.

Content

No source excerpt is available for this finding.

Missing User Warnings

Low
Category
Not specified by scanner
Confidence
90% confidence
Finding

The README describes fetching Amazon reviews through the Reveyes API but does not clearly warn users that their ASINs, marketplace selections, and resulting review content are transmitted to and processed by a third-party service. This creates a transparency and privacy risk: users may unknowingly send potentially sensitive research targets or business-intelligence queries to an external provider.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Low
Category
Not specified by scanner
Confidence
78% confidence
Finding

The skill instructions are written as Chinese-first usage guidance ('Use when' conditions expressed for Chinese-speaking users), while example outputs include English review text, and there is no statement that users may choose their preferred language. This can create a locale/language policy issue because the skill appears tailored to a specific language context without explicit opt-in or alternatives.

Content

No source excerpt is available for this finding.