Openclaw Wechat Mp Guide

• Outcome: Review
• Latest audit: Sep 12, 2026 · 1:24 AM UTC
• Version: 1.0.1

Overview

This is a coherent WeChat setup guide, but it gives an unsafe fixed callback token example and under-explains privacy risks for automated message and image processing.

Review before installing or following this guide. Generate a unique high-entropy WeChat callback token and do not use the documented example value. Operators should also publish clear user-facing notice that messages and images may be processed by AI, limit retention/logging, and handle AppSecret, token, and EncodingAESKey as protected secrets.

A.I.G

Vulnerability Patterns

• Insecure Skill Coding Practices: Finds exploitable flaws such as hardcoded secrets or command injection
• Skill Instruction Hijacking: Alters the agent's session goals or safety constraints when the skill loads
• Agent Memory Poisoning: Writes attacker-controlled rules into memory that affect later sessions
• Remote Payload Retrieval and Execution: Fetches external code whose behavior can change after review
• Embedded Malicious Code: Ships malicious scripts inside the skill and executes them locally

Findings (1)

T09 · Insecure Skill Coding Practices

Warning

• Location: SKILL.md:37

Finding

Predictable Shared WeChat Verification Token

Content

View full analysis

Vulnerability Details

File Location: SKILL.md, lines 37 and 51
Vulnerability Type: Predictable authentication token in configuration guidance
Risk Level: Medium

Vulnerable Code Snippet

text

Token: openclaw2024

The same predictable value is presented as an example at line 37 and as direct configuration input at line 51.

Technical Analysis

The guide recommends a fixed, low-entropy token without clearly requiring users to replace it with a unique, cryptographically random secret. Users following the instructions verbatim may deploy the publicly documented value as the verification token for their WeChat callback endpoint.

Callback authentication commonly relies on this token when validating request signatures. If the token is known, an attacker may be able to generate otherwise valid authentication signatures and submit forged requests to the public /wechat endpoint. Encrypted message contents may remain protected by the separately configured EncodingAESKey, but the documented token can still undermine any request-validation mechanism that relies on the token alone.

The practical exploitability and exact consequences depend on the callback-validation behavior implemented by OpenClaw, which is not included in this project.

Attack Path

1. An operator follows the guide and configures openclaw2024 as the production WeChat verification token.
2. The operator exposes the documented /wechat callback endpoint on a public server.
3. An attacker obtains the token directly from this publicly distributed guide.
4. The attacker generates requests or verification signatures using the known token.
5. The attacker submits crafted requests to the callback endpoint.
6. If OpenClaw treats a valid token-derived signature as sufficient authentication, the forged request is accepted and processed.

Impact Assessment

A successful attack could allow unauthorized callback verification, forge ...[truncated 362 chars]

Remediation

View remediation

Remediation Suggestions

1. Replace the fixed example with an unmistakable placeholder such as YOUR_UNIQUE_RANDOM_TOKEN.

2. Require every deployment to generate an independent token using a cryptographically secure random-number generator, with at least 32 random bytes of entropy.

3. Provide a safe generation example, such as:

   bash

   openssl rand -hex 32
   

4. Explicitly warn users never to copy documentation examples into production.

5. Store the token, AppSecret, and EncodingAESKey in a protected secret manager or restricted environment variables rather than plaintext configuration where supported.

6. Restrict secret-file permissions to the service account and prevent secrets from appearing in logs, shell history, screenshots, or source control.

7. Validate every callback signature using constant-time comparison and reject stale timestamps, replayed nonces, malformed requests, and requests that fail encryption or signature checks.

8. Rotate any deployment currently using openclaw2024 and review callback logs for suspicious verification or message-processing requests.

SkillSpector

Vulnerability Patterns

• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access
• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• Excessive Agency: Unrestricted Tool Access, Autonomous Decision Making, Scope Creep

Findings (2)

Missing User Warnings

Medium

• Category: Not specified by scanner
• Confidence: 92% confidence

Finding

The skill instructs operators to enable automatic replies for any公众号 message, but it does not disclose that end-user message content will be transmitted to and processed by an AI system. This creates a privacy and compliance risk because users may unknowingly share personal, sensitive, or regulated data with automated systems, especially in customer-service contexts.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium

• Category: Not specified by scanner
• Confidence: 94% confidence

Finding

The image-recognition feature encourages processing user-uploaded media without warning that images may contain personal, biometric, location, or other sensitive information. Without disclosure and handling guidance, operators could unintentionally collect and process sensitive media in ways that violate user expectations, platform rules, or privacy regulations.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.