// Enterprise AI security - OWASP LLM Top 10, prompt injection defense, guardrails, PII protection
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| name | AI Security Expert |
| description | Enterprise AI security - OWASP LLM Top 10, prompt injection defense, guardrails, PII protection |
| version | 1.1.0 |
| last_updated | "2026-01-06T00:00:00.000Z" |
| external_version | OWASP LLM Top 10 v2 |
| resources | resources/security-patterns.py |
| triggers | ["AI security","prompt injection","LLM security","guardrails","PII protection"] |
Enterprise AI security architect specializing in securing LLM applications, defending against prompt injection, implementing guardrails, and OWASP LLM Top 10 compliance.
| # | Vulnerability | Risk | Key Defense |
|---|---|---|---|
| LLM01 | Prompt Injection | Critical | Input sanitization, delimiters |
| LLM02 | Insecure Output | High | Output validation, sanitization |
| LLM03 | Training Data Poisoning | High | Data provenance, auditing |
| LLM04 | Model DoS | Medium | Rate limiting, timeouts |
| LLM05 | Supply Chain | High | Verification, pinning |
| LLM06 | Sensitive Info Disclosure | High | PII detection, redaction |
| LLM07 | Insecure Plugin Design | High | Permission model, validation |
| LLM08 | Excessive Agency | High | Human-in-the-loop, least privilege |
| LLM09 | Overreliance | Medium | Confidence scores, citations |
| LLM10 | Model Theft | Medium | Rate limiting, watermarking |
Attack Types:
Defense Pattern:
User Input โ Sanitize โ Delimit โ LLM โ Validate Output โ Filter
Code patterns: resources/security-patterns.py
| Type | Example Pattern |
|---|---|
*@*.com | |
| Phone | XXX-XXX-XXXX |
| SSN | XXX-XX-XXXX |
| Credit Card | 16 digits |
| IP Address | X.X.X.X |
define user express harmful intent
"How do I hack"
define bot refuse harmful request
"I can't help with that."
define flow harmful intent
user express harmful intent
bot refuse harmful request
guard = Guard().use_many(
ToxicLanguage(on_fail="fix"),
PIIFilter(on_fail="fix"),
ValidJSON(on_fail="reask")
)
Input Guards โ LLM Call โ Output Guards โ Response
Implementation: resources/security-patterns.py
| Layer | Controls |
|---|---|
| Network | WAF, DDoS protection, API gateway |
| Auth | OAuth 2.0, API keys, mTLS |
| Input | Schema validation, injection detection |
| Guardrails | Topic restrictions, PII filtering |
| Model | Versioning, anomaly detection |
| Output | Response filtering, fact verification |
| Audit | Logging, retention, compliance |
Test suite: resources/security-patterns.py
| Incident | Severity | Response |
|---|---|---|
| Prompt injection detected | Medium | Block, log, analyze |
| Data exfiltration attempt | High | Block, forensics, notify |
| Model extraction detected | High | Rate limit, investigate |
Secure AI systems with defense in depth and zero trust principles.