Security Testing & Next Steps
Next Steps
2 min read
Congratulations on completing AI Security Fundamentals! You now understand the threats facing LLM applications and how to defend against them. Here's how to continue your security journey.
What You've Learned
┌─────────────────────────────────────────────────────────────┐
│ Course Summary │
│ │
│ Module 1: AI Security Landscape │
│ ✓ Why security matters for LLM applications │
│ ✓ OWASP LLM Top 10 vulnerabilities │
│ ✓ Attack surfaces and threat modeling │
│ │
│ Module 2: Prompt Injection │
│ ✓ Direct and indirect injection techniques │
│ ✓ Jailbreaking attacks │
│ ✓ Advanced obfuscation methods │
│ │
│ Module 3: Other Vulnerabilities │
│ ✓ System prompt leakage │
│ ✓ Data exposure risks │
│ ✓ RAG and agent vulnerabilities │
│ │
│ Module 4: Building Guardrails │
│ ✓ Input validation strategies │
│ ✓ Output sanitization │
│ ✓ NeMo Guardrails and LLaMA Guard │
│ │
│ Module 5: Production Security │
│ ✓ Defense in depth architecture │
│ ✓ Monitoring and logging │
│ ✓ Secure agent design │
│ │
│ Module 6: Testing & Next Steps │
│ ✓ Red team testing │
│ ✓ Security checklists │
│ ✓ Staying current with threats │
└─────────────────────────────────────────────────────────────┘
Your Security Skills
You can now:
- Identify LLM security vulnerabilities using OWASP framework
- Implement input validation and output sanitization
- Configure guardrails using NeMo and LLaMA Guard
- Design secure agents with least privilege
- Test defenses with red team techniques
- Set up monitoring and alerting
Recommended Learning Path
┌─────────────────────────┐
│ AI Security Fundamentals │
│ (You are here!) │
└───────────┬─────────────┘
│
▼
┌────────────────────┴────────────────────┐
│ │
▼ ▼
┌───────────────────┐ ┌───────────────────┐
│ Red Teaming AI │ │ Secure AI in Prod │
│ Systems │ │ (Advanced) │
│ │ │ │
│ • Attack methods │ │ • CI/CD security │
│ • Vuln discovery │ │ • Compliance │
│ • Adversarial ML │ │ • Incident resp │
└───────────────────┘ └───────────────────┘
Immediate Action Items
| Priority | Action | Timeline |
|---|---|---|
| 1 | Audit your current LLM applications | This week |
| 2 | Implement basic input validation | This week |
| 3 | Set up monitoring and logging | Next week |
| 4 | Run red team tests on your apps | Next 2 weeks |
| 5 | Review and update regularly | Ongoing |
Security Audit Starter
Apply what you learned immediately:
def quick_security_audit(app_config: dict) -> dict:
"""Quick security audit based on course content."""
findings = {
"critical": [],
"high": [],
"medium": [],
"low": [],
}
# Check input validation
if not app_config.get("input_validation_enabled"):
findings["critical"].append(
"Input validation not enabled - vulnerable to prompt injection"
)
# Check output sanitization
if not app_config.get("output_sanitization"):
findings["high"].append(
"Output not sanitized - vulnerable to XSS"
)
# Check rate limiting
if not app_config.get("rate_limiting"):
findings["high"].append(
"No rate limiting - vulnerable to abuse"
)
# Check monitoring
if not app_config.get("logging_enabled"):
findings["medium"].append(
"Logging not enabled - can't detect attacks"
)
# Check guardrails
if not app_config.get("guardrails_enabled"):
findings["high"].append(
"No guardrails configured - limited protection"
)
# Generate report
total_issues = sum(len(f) for f in findings.values())
risk_level = "Critical" if findings["critical"] else \
"High" if findings["high"] else \
"Medium" if findings["medium"] else "Low"
return {
"findings": findings,
"total_issues": total_issues,
"risk_level": risk_level,
"recommendation": "Address critical and high issues immediately"
}
# Audit your app
my_app = {
"input_validation_enabled": True,
"output_sanitization": False,
"rate_limiting": True,
"logging_enabled": True,
"guardrails_enabled": False,
}
audit_result = quick_security_audit(my_app)
print(f"Risk Level: {audit_result['risk_level']}")
print(f"Total Issues: {audit_result['total_issues']}")
Resources to Bookmark
Documentation
- OWASP LLM Top 10: genai.owasp.org/llm-top-10
- NeMo Guardrails: docs.nvidia.com/nemo/guardrails
- LangChain Security: python.langchain.com/docs/security
Tools
- Garak (LLM vulnerability scanner)
- Rebuff (Prompt injection detection)
- LLaMA Guard (Safety classifier)
Communities
- OWASP AI Security
- AI Village (DEF CON)
- r/MachineLearning security threads
Keep Building Securely
Remember the core principles:
- Never trust user input - Always validate and sanitize
- Defense in depth - Multiple layers of protection
- Least privilege - Minimum permissions for agents
- Monitor everything - You can't defend what you can't see
- Stay current - Threats evolve, so must defenses
Thank You!
You've taken an important step in securing AI applications. The skills you've learned here will help protect users, data, and systems as AI becomes more prevalent.
What's Next?
Continue your AI security journey with advanced topics:
- Red Teaming AI Systems - Learn to think like an attacker
- Secure AI in Production - Enterprise-scale security patterns
- AI Safety Research - Cutting-edge safety techniques
Your AI security journey has just begun. Stay curious, stay vigilant, and build systems that users can trust. :::