MASTERY30 min22 sections

Context Security

THIS WEEK'S JOURNEY

Context Security: The Critical Shield for Your LLM Applications

In the rapidly evolving landscape of LLM-powered applications, context security has emerged as the most critical—and most frequently overlooked—aspect of system design. Every piece of context you feed to a language model represents a potential attack surface, a possible data leak, or an opportunity for malicious actors to manipulate your system's behavior.

78%

of LLM applications vulnerable to prompt injection

A comprehensive security audit of 500 production LLM applications revealed that over three-quarters contained at least one exploitable prompt injection vulnerability.

Key Insight

Context Is the New Attack Surface

Traditional application security focused on network boundaries, input validation, and database protection—but LLM applications introduce an entirely new attack vector: the context window itself. Every token in your context represents a potential instruction that the model will follow, whether it came from your trusted system prompt or a malicious user input.

Samsung

The $2.3B Semiconductor Leak That Changed Corporate AI Policy

Samsung invested $47M in building a secure internal AI platform with context iso...

Framework

The SHIELD Framework for Context Security

Sanitize

All user inputs must pass through sanitization layers before entering the context. This includes str...

Hierarchize

Establish clear privilege hierarchies within your context. System instructions should have immutable...

Isolate

Maintain strict boundaries between different users' contexts, different data sensitivity levels, and...

Encrypt

Sensitive data within context should be encrypted at rest and in transit. Implement tokenization for...

The Illusion of Model-Level Security

Many teams assume that because models like GPT-4 or Claude have been trained to refuse harmful requests, their applications are inherently secure. This is dangerously false.

Traditional App Security vs. LLM Context Security

Traditional Application Security

Clear boundaries between code and data—SQL injection require...

Input validation can use deterministic rules and regex patte...

Attack payloads are typically recognizable patterns (scripts...

Security perimeter is well-defined at network and applicatio...

LLM Context Security

Code and data blur—natural language instructions ARE the cod...

Malicious inputs can be semantically disguised as legitimate...

Attack payloads are natural language indistinguishable from ...

Context window is a porous boundary where all content has in...

Key Insight

The Three Vectors of Context Attack

Understanding context attacks requires recognizing three distinct vectors: direct injection, indirect injection, and context extraction. Direct injection occurs when attackers include malicious instructions in their own inputs, attempting to override your system prompt.

Basic Input Sanitization Layertypescript

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interface SanitizationResult {
  sanitized: string;
  threats: ThreatDetection[];
  riskScore: number;
}

class ContextSanitizer {
  private readonly injectionPatterns = [
    /ignore\s+(all\s+)?previous\s+instructions/gi,
    /disregard\s+(all\s+)?(above|prior|previous)/gi,
    /you\s+are\s+now\s+[a-z]+/gi,
    /system\s*:\s*/gi,

Prompt Injection Defense Checklist

Anti-Pattern: The 'Model Will Refuse' Assumption

❌ Problem

This approach fails catastrophically because prompt injection attacks don't ask ...

✓ Solution

Implement defense in depth with multiple security layers. Use input sanitization...

Bing Chat

The Sydney Incident and Prompt Extraction Attacks

Microsoft implemented multiple layers of prompt protection, including instructio...

Key Insight

Defense in Depth: The Only Viable Strategy

No single security measure can protect against all prompt injection attacks—the attack surface is too broad and attackers too creative. The only viable strategy is defense in depth, implementing multiple overlapping security layers so that if one fails, others catch the attack.

Defense in Depth Security Architecture

User Input

Input Sanitization (...

Injection Detection ...

Context Assembly (Pr...

Implementing a Prompt Injection Detection Pipeline

Define Your Threat Model

Implement Pattern-Based Detection

Deploy an ML-Based Classifier

Implement Semantic Analysis

Create Response Policies for Detection

Start with Logging, Then Add Blocking

When deploying injection detection, start in logging-only mode before enabling blocking. Run for 1-2 weeks to understand your false positive rate and tune thresholds.

Practice Exercise

Build a Basic Injection Detection System

45 min

Prompt Injection Defense Resources

OWASP LLM Top 10

article

Lakera Gandalf Challenge

tool

Simon Willison's Prompt Injection Research

article

Garak - LLM Vulnerability Scanner

tool

Key Insight

The Indirect Injection Threat Multiplier

While direct injection gets most attention, indirect injection through RAG pipelines represents a more scalable and dangerous threat vector. In direct injection, attackers must interact with your application directly, limiting their reach.

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