WHITEPAPER

AI Security Best Practices: Protecting LLMs in Production

A technical deep-dive into securing large language models against prompt injection, data exfiltration, and emerging attack vectors.

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35 pages of technical security guidance from our research team

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Abstract

Large language models (LLMs) have become critical infrastructure for enterprises, powering customer service, code generation, document analysis, and countless other applications. However, the unique architecture of LLMs creates novel security challenges that traditional application security tools cannot address.

This whitepaper examines the attack surface of LLM applications, categorizes known attack vectors, and provides actionable recommendations for securing AI systems in production environments.

Key Topics Covered

Prompt Injection Attacks

Analysis of 80+ documented prompt injection techniques, including:

Direct prompt injection via user input
Indirect injection through external data sources
Jailbreak techniques and their evolution
Detection and prevention strategies

Data Exfiltration Risks

How sensitive data can leak through LLM applications:

PII leakage in model outputs
Training data extraction attacks
System prompt disclosure
Side-channel information leaks

RAG-Specific Vulnerabilities

Security considerations for retrieval-augmented generation:

Document injection attacks
Access control bypass
Poisoned knowledge bases
Citation manipulation

Agent Security

Protecting AI agents with tool access:

Tool call validation
Permission boundaries
Audit trail requirements
Sandboxing strategies

Defense in Depth Architecture

The whitepaper presents a comprehensive defense-in-depth architecture for LLM applications:

Input Layer        [User Input] --> [Input Validation] --> [PII Detection]
                                            |
                                            v
Model Layer        [Prompt Shield] --> [LLM] --> [Output Filter]
                                            |
                                            v
Agent Layer        [Tool Validator] --> [Action] --> [Audit Log]
                                            |
                                            v
Data Layer         [RAG Guard] --> [Vector DB] --> [Access Control]

Recommendations Summary

Implement input validation - Filter malicious patterns before they reach the model
Deploy PII detection - Prevent sensitive data from being processed or logged
Use output filtering - Catch data leakage in model responses
Secure RAG pipelines - Validate document sources and access permissions
Limit agent capabilities - Apply principle of least privilege to AI agents
Monitor and audit - Maintain complete logs for security analysis

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