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
# 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()}
- Define Objectives — Clarify the goals and scope for agent hardening.
- Gather Resources — Collect tools, data, and access needed for agent hardening.
- Execute Process — Carry out agent hardening operations methodically.
- Verify Quality — Check results against acceptance criteria.
- Document Outcomes — Record findings, decisions, and next steps.
Tools
- Analysis Platform — Data processing and visualization
- Collaboration Tools — Team coordination and knowledge sharing
Process
- Reconnaissance — Gather target information, identify attack surface, enumerate services
- Analysis/Exploitation — Execute the technique, analyze results, document findings
- 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. |