Skill Guide

Security patterns for AI middleware (PII scrubbing, prompt injection defense, access control)

Security patterns for AI middleware are pre-defined architectural and operational strategies designed to protect LLM-powered systems by systematically removing sensitive data, blocking adversarial inputs, and enforcing granular user permissions.

This skill is critical for enabling the safe deployment of generative AI in regulated industries (finance, healthcare, legal), directly reducing compliance risk and preventing catastrophic data leakage. Mastery ensures AI middleware acts as a reliable, trust boundary rather than a vulnerability amplifier.

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How to Learn Security patterns for AI middleware (PII scrubbing, prompt injection defense, access control)

1. Understand core data classification (PII vs. PHI vs. internal). 2. Study basic LLM threat models, focusing on OWASP Top 10 for LLMs. 3. Implement a simple proxy that logs requests and applies regex-based PII masking before forwarding to an API like OpenAI.

1. Deploy a real middleware solution (e.g., using Python/FastAPI) that integrates with an LLM API, implementing context-aware PII scrubbing using NER models (spaCy, Presidio). 2. Build and test prompt injection defenses using techniques like input/output filtering and delimiter-based prompt hardening. 3. Design a basic RBAC (Role-Based Access Control) system with token-based authentication (JWT) to manage middleware access. Common mistake: relying solely on regex for PII, missing contextual entities.

1. Architect a zero-trust middleware layer for a multi-tenant LLM application, incorporating data loss prevention (DLP) pipelines and audit trails. 2. Design and enforce fine-grained ABAC (Attribute-Based Access Control) policies based on user attributes, data sensitivity, and request context. 3. Develop a continuous security testing framework (red teaming) that proactively tests for novel injection vectors and data exfiltration paths. Mentor others on threat modeling specific to RAG (Retrieval-Augmented Generation) architectures.

Practice Projects

Beginner

Project

PII-Scrubbing Proxy for a Public LLM API

Scenario

Your team wants to use a public LLM API for internal customer service tasks, but cannot send raw customer data (emails, phone numbers, SSNs) due to policy.

How to Execute

1. Set up a simple reverse proxy (using Flask or FastAPI) that sits between your application and the LLM API. 2. Integrate a lightweight PII detection library (like `pii-analyzer` or Presidio) to identify and replace PII in the request body with placeholders (e.g., [EMAIL_01]). 3. After receiving the LLM response, use the stored mapping to reverse the replacement in the output. 4. Log all requests/responses with PII masked for auditing.

Intermediate

Project

Implementing a Prompt Injection Firewall

Scenario

Your middleware handles user-submitted prompts for a content generation tool. Attackers are attempting prompt injection to make the LLM output harmful content or reveal system prompts.

How to Execute

1. Implement input sanitization: Use a combination of allow-listing (permitting only safe characters) and heuristic filters to detect known injection patterns (e.g., 'Ignore previous instructions'). 2. Apply prompt hardening: Structure system prompts with clear delimiters and instruction hierarchy to separate user input from system instructions. 3. Deploy an output monitor: Use a secondary LLM call or classifier to analyze the generated response for policy violations (toxicity, secret leakage) before returning it to the user. 4. Maintain a test suite of known attack vectors for continuous validation.

Advanced

Project

Multi-Tenant AI Gateway with ABAC & Audit

Scenario

You are building a centralized AI middleware platform for a large enterprise, serving multiple departments (HR, Finance, R&D) with different data sensitivity levels and usage policies.

How to Execute

1. Design an ABAC engine where access decisions are based on attributes like user role, department, data classification level of the target dataset, and request purpose. 2. Integrate the middleware with the company's identity provider (e.g., Okta, Azure AD) and a secrets manager for secure credential handling. 3. Implement end-to-end encryption and comprehensive audit logging that captures user identity, input/output hashes, and policy decisions for non-repudiation. 4. Develop a red-team playbook to regularly simulate insider threat scenarios and data exfiltration attempts through the middleware.

Tools & Frameworks

Software & Libraries

Microsoft Presidio (PII Detection & Anonymization)LangChain / LlamaIndex (Middleware orchestration frameworks)OWASP LLM Top 10 & AI Security Guidelines

Use Presidio for robust, configurable PII/PHI entity recognition and redaction. Leverage LangChain/LlamaIndex to structure the middleware pipeline (input -> sanitize -> augment -> call LLM -> sanitize output). Reference OWASP resources for standardized threat models and control mappings.

Infrastructure & Platforms

API Gateway (Kong, AWS API Gateway, Envoy)Identity-Aware Proxy (Google IAP, Cloudflare Access)Secrets Management (HashiCorp Vault, AWS Secrets Manager)

Use an API gateway for rate limiting, authentication, and basic WAF-like rule enforcement at the network edge. Implement an Identity-Aware Proxy for zero-trust access patterns to the middleware endpoint. Use a secrets manager to dynamically inject LLM API keys and other credentials, avoiding hardcoding.

Testing & Validation

Red Team Playbooks for LLMsOWASP ZAP (for proxy-based security testing)Garak (LLM vulnerability scanner)

Use specialized red team playbooks and tools like Garak to systematically test middleware defenses against prompt injection, jailbreaking, and data leakage. Integrate security scans into the CI/CD pipeline for the middleware code itself.

Interview Questions

Answer Strategy

The candidate should demonstrate a layered defense approach. Structure the answer around: 1) Pre-retrieval: Apply ABAC to the user's query itself, attaching permissions metadata. 2) Retrieval: Implement document-level access control in the vector database, ensuring only permitted chunks are retrieved. 3) Post-generation: Use an output monitor to perform a semantic similarity check between the generated answer and the retrieved chunks, flagging or redacting answers that are overly specific quotes from restricted documents.

Answer Strategy

The interviewer is testing the candidate's ability to mentor and their understanding of nuanced security controls. The answer should acknowledge the blocklist's limited value as a surface-level filter, then pivot to a defense-in-depth strategy: 'While a blocklist can stop naive attacks, sophisticated injections are semantic, not lexical. A better strategy involves input sanitization to remove non-standard characters, prompt hardening with clear system instruction delimiters, and a critical output guardrail that uses a secondary classifier or LLM call to analyze the final response for harmful or off-policy content before it's returned to the user.'