A comprehensive framework for protecting AI agents against prompt injection and securing external content processing.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-security-audit
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install agent-security-audit using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Agent Security Audit?
Agent Security Audit is a specialized toolkit designed to fortify AI agents when they interact with untrusted external content. As agents increasingly browse the web and process third-party data, they become vulnerable to prompt injection attacks. This skill provides the architectural blueprints and scripts necessary to implement a multi-layered defense system. By utilizing these Openclaw Skills, developers can establish a strict instruction hierarchy, ensuring that system prompts always take precedence over external inputs.
The framework covers everything from basic content purification to advanced memory protection. It enables agents to distinguish between trusted user instructions and potentially malicious commands hidden in web pages, emails, or files. Implementing this security layer is essential for any developer building autonomous agents that handle real-world data streams.
Agent Security Audit Use Cases
- Protecting AI agents from malicious system overrides hidden in web content.
- Sanitizing external files and HTML data before processing them through a LLM.
- Implementing honeypot responses to misdirect and log attackers attempting prompt injection.
- Securing agent memory and persistent storage from unauthorized 'write' instructions.
How Agent Security Audit Works
- Instruction Hierarchy: The system defines clear boundaries between system-level instructions and untrusted external content.
- Content Sanitization: External data is passed through a bash-based pipeline that strips HTML comments, zero-width characters, and obfuscated Base64 strings.
- Safe Fetching: A secure retrieval pattern is used to limit content length and log all external access attempts for auditing.
- Injection Detection: Pattern matching scans inputs for high-risk keywords like ADMIN OVERRIDE or behavior change requests.
- Memory Guarding: Every write operation to the agent's memory or local files is validated against the trust level of the data source.
Agent Security Audit Setup
To deploy these security measures within your agent environment, integrate the provided shell scripts into your content processing pipeline. These Openclaw Skills are designed to be portable across Unix-like environments.
# Initialize the content sanitizer
chmod +x safe-content-processor.sh
# Run a test sanitization on external input
./safe-content-processor.sh /tmp/external-content.html /tmp/safe-output.txt
You should also update your Nginx or proxy configuration to filter suspicious patterns at the network edge as demonstrated in the implementation guide.
Agent Security Audit Data Schema & Taxonomy
The skill manages security through a structured logging and validation schema to keep your Openclaw Skills organized.
| Component | Purpose | Location/Format |
|---|---|---|
| Security Logs | Records detected injection attempts and patterns | /var/log/security.log |
| Fetch Logs | Tracks all external URL requests and character counts | /var/log/fetch.log |
| Sanitized Buffer | Temporary storage for cleaned external content | /tmp/fetch-output.txt |
| Pattern List | Array of regex patterns for injection detection | Inside injection-detector.sh |
| Memory Guard | Validation logic for persistent state updates | memory-guard.sh |
Agent Security Audit Advanced Features
- Honeypot Responses: Automatically generates fake success messages to trick attackers while logging their specific injection vectors.
- Zero-Width Character Stripping: Detects and removes invisible characters used to bypass traditional filters.
- Multi-Level Defense Checklist: Provides a roadmap from basic Level 1 protection to advanced Level 3 dynamic threat detection.
- Nginx Integration: Pre-configured blocks for intercepting malicious payloads before they ever reach the AI agent's logic.
SKILL.md
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