Use when secure AI agents against prompt injection, jailbreaking, data
Scanned 9/8/2026
Install to Claude Code
npx -y skills add oyi77/1ai-skills --skill security-agent-hardening --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: security-agent-hardening
description: Use when secure AI agents against prompt injection, jailbreaking, data
exfiltration, and supply chain attacks. Implement guardrails, sandboxing, and monitoring
for safe autonomous operation. Use when working with security agent hardening.
domain: cybersecurity
author: oyi77
license: Apache-2.0
subdomain: general-cybersecurity
tags:
- agent-security
- prompt-injection
- guardrails
- sandboxing
- llm-security
- ai-safety
version: 1.0.0
category: cybersecurity
---
# Security Agent Hardening
## Overview
Cybersecurity skill for security agent hardening. Follows industry best practices and security standards.
## When to Use
**Trigger phrases:**
- "Harden this agent against attacks"
- "Implement guardrails for autonomous agents"
- "Prevent prompt injection in my system"
- "Sandbox agent execution"
- "Audit agent security"
- "Secure LLM applications"
**Use cases:**
- Production AI agent deployment
- Customer-facing chatbots
- Autonomous code generation
- Multi-agent systems
- Tool-using agents (MCP, function calling)
**When NOT to use:**
- Internal research agents with no external input
- Fully human-in-the-loop systems
- Agents without tool access
## When NOT to Use
- When you lack proper authorization for testing
- For production systems without change management
- When the task requires legal or compliance expertise beyond technical scope
## Prerequisites
- Access to relevant log sources and security tools
- Understanding of agent hardening fundamentals
- Appropriate permissions for data access and tool operation
## Workflow
```python
# Example: IOC detection
import re
IOC_PATTERNS = {
"ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
"domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
"hash_md5": r"\b[a-f0-9]{32}\b",
"hash_sha256": r"\b[a-f0-9]{64}\b",
}
def extract_iocs(text: str) -> dict:
return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
```
1. **Define Objectives** — Clarify the goals and scope for agent hardening.
2. **Gather Resources** — Collect tools, data, and access needed for agent hardening.
3. **Execute Process** — Carry out agent hardening operations methodically.
4. **Verify Quality** — Check results against acceptance criteria.
5. **Document Outcomes** — Record findings, decisions, and next steps.
## Tools
- **Analysis Platform** — Data processing and visualization
- **Collaboration Tools** — Team coordination and knowledge sharing
## Process
1. **Reconnaissance** — Gather target information, identify attack surface, enumerate services
1. **Analysis/Exploitation** — Execute the technique, analyze results, document findings
1. **Reporting** — Document IOCs, write findings, provide remediation recommendations
## Verification
- [ ] All agent hardening procedures executed completely and documented
- [ ] Findings validated against multiple data sources
- [ ] False positives identified and filtered
- [ ] Results documented with evidence and timestamps
- [ ] Recommendations provided with risk-based prioritization
## Anti-Rationalization Table
| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
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