A self-contained security testing suite designed to validate an AI agent's input sanitization and defense against common injection attacks.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-hardening
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-hardening 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 Hardening?
Agent Hardening is a critical security utility designed for developers who need to verify the integrity and safety of their AI agent workflows. By leveraging Openclaw Skills, this tool performs rigorous, self-contained checks on input data to identify potential vulnerabilities such as invisible Unicode characters, hidden HTML directives, and bidirectional text overrides.
The primary focus of this skill is to provide a safe, synthetic testing environment. It ensures that your agent can handle malicious input patterns without ever accessing local files, configurations, or system memory. This makes it an essential part of the development lifecycle for anyone building production-ready AI applications that require high standards of data sanitization.
Agent Hardening Use Cases
- Validating that an agent's environment effectively strips invisible Unicode control characters that could bypass filters.
- Detecting hidden HTML comment injections that might attempt to smuggle system instructions into standard text.
- Identifying bidirectional (Bidi) override characters used to spoof filenames or disguise malicious file paths.
- Benchmarking security protocols against community-maintained threat definitions within the Openclaw Skills ecosystem.
- Performing routine security audits on input processing pipelines without risking exposure of real user data.
How Agent Hardening Works
- The skill executes a series of Python-based diagnostic scripts using hardcoded synthetic data samples.
- It tests Unicode sanitization by attempting to pass invisible characters like zero-width joiners and BOM injections through a sanitization lambda.
- It runs regex-based scans to detect hidden HTML directives that could potentially influence the underlying LLM's behavior.
- It evaluates string inputs for Bidi override characters (U+202E, etc.) to ensure that spoofed filenames are flagged.
- It compares the local test results against a taxonomy of known threats to provide a pass/fail report for the agent's hardening status.
Agent Hardening Setup
To implement these security checks, ensure your environment has Python 3.3+ installed. You can integrate these Openclaw Skills tests into your CI/CD pipeline or run them manually using the following structure:
# Example: Run the Unicode Control Character Sanitization test
python3 -c "import unicodedata; samples=[('ZWJ', '\u200d')]; ..."
No additional external dependencies or API keys are required for the core hardening checks.
Agent Hardening Data Schema & Taxonomy
The skill utilizes a strictly synthetic data schema to ensure no sensitive information is processed. The taxonomy is organized as follows:
| Component | Type | Description |
|---|---|---|
| Synthetic Samples | Array | Predefined strings containing known injection patterns. |
| Sanitization Logic | Function | Lambda functions used to strip Cf (Other, format) Unicode categories. |
| Threat Patterns | Regex | DOTALL regex patterns used to identify hidden HTML comments. |
| Validation Flags | Boolean | Indicators of whether a specific input string is considered clean or malicious. |
All results generated by these Openclaw Skills are ephemeral and do not persist in local databases.
Agent Hardening Advanced Features
- Zero-dependency architecture ensuring the security suite can run in air-gapped or restricted environments.
- Support for identifying complex Unicode spoofing techniques, including invisible separators and BOM injections.
- Integration links for community threat definitions to keep your agent protected against evolving injection techniques.
- High-performance regex detection for hidden system directives nested within legitimate-looking text.
- Compatibility with various Openclaw Skills to provide a layered defense strategy for agent-based automation.
SKILL.md
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