# Security Agent Hardening

> 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.

- Skill: `oyi77/security-agent-hardening` (Agent Skill)
- Install (CLI): `npx skillmds add oyi77/security-agent-hardening`
- Raw SKILL.md: https://api.skillmd.com/api/skills/oyi77/security-agent-hardening/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: Apache-2.0
- Author: oyi77 (https://skillmd.com/u/oyi77)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/oyi77/security-agent-hardening

---


# 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. |