# defense
215 articlestagged with “defense”
Advanced Defense Practice Exam
Practice exam on guardrail architecture, constitutional AI defenses, and behavioral monitoring implementation.
Defense Specialist Practice Exam
Practice exam for AI defense specialization covering guardrails, monitoring, and incident response.
Defense Specialist Practice Exam (Assessment)
Specialized practice exam for AI defense practitioners covering guardrails, monitoring, and architecture.
Defense Fundamentals Assessment
Test your understanding of AI defense mechanisms including input/output filtering, guardrails, sandboxing, and defense-in-depth strategies with 9 intermediate-level questions.
Defense & Mitigation Assessment
Assessment covering guardrails, classifiers, constitutional AI, and defense-in-depth architectures.
Defense & Mitigation Assessment (Assessment)
Test your knowledge of AI guardrails, monitoring systems, incident response, and defense-in-depth strategies with 15 intermediate-level questions.
Multimodal Defense Assessment
Assessment covering defenses against visual injection, audio attacks, and cross-modal exploitation.
Output Safety Assessment
Assessment of output filtering, content classification, watermarking, and data leakage prevention.
Defense Architecture Assessment
Assessment on designing defense-in-depth architectures for different AI application types.
Injection Defense Assessment
Assessment on defending against prompt injection including classifiers, guardrails, and output filtering.
Skill Verification: Defense Effectiveness Evaluation
Practical verification of skills in evaluating guardrails, classifiers, and monitoring systems.
Skill Verification: Defense Evaluation
Hands-on verification of ability to evaluate and bypass LLM defense mechanisms.
Skill Verification: Multimodal Defense
Practical verification of ability to defend multimodal AI systems against cross-modal attacks.
Skill Verification: Defense Implementation
Timed skill verification lab: build a working guardrail system that passes automated attack tests within 45 minutes.
Defense Specialist Study Guide
Study guide for defense assessments covering guardrails, classifiers, monitoring, and architecture design.
Capstone: Defense Architecture Design
Capstone exercise: design and validate a defense-in-depth architecture for an LLM-powered application.
Capstone: Build an LLM Firewall and Guardrails System
Design and implement a layered LLM firewall that inspects, filters, and enforces policies on both inputs and outputs of language model applications.
Capstone: Defense System Implementation
Build a complete AI defense stack with input filtering, output monitoring, guardrails, rate limiting, and logging, then evaluate it against automated attacks.
AI Code Review Manipulation
Techniques for tricking AI-powered code review tools into approving vulnerable code — covering semantic obfuscation, review bias exploitation, and defense strategies.
April 2026: Defense Building Challenge
Build the most robust defense system for a chatbot, scored against an automated attack suite of 500 diverse prompt injection and jailbreak attempts.
Defense Builders Series
Community series focused on building and testing defensive mechanisms against known attack classes.
Community Project: Defense Library
Open-source library of composable defense components contributed by the community, with standardized interfaces and comprehensive documentation.
Community Challenge: Defense Gauntlet
Build AI chatbot defenses that survive 100 automated attack attempts across diverse attack categories, scored on block rate and usability preservation.
Defense Challenge: Detection Engineering
Challenge focused on building detection systems for prompt injection, with scoring based on true positive rate and false positive rate.
Defense Challenge: Build Unbreakable Guardrails
A challenge where participants build guardrail systems that must withstand automated attack suites, scored on both security and usability metrics.
Monthly Challenge: Defense Builders
Monthly challenge to build the most robust defense against a specified set of attack techniques.
Tool Building Hackathon: Defense Toolkit
Collaborative hackathon for building open-source defense tools including guardrails, filters, and monitoring components for LLM applications.
Weekly Challenge: Defense Reverse Engineering
Weekly challenges where participants reverse-engineer unknown defense mechanisms to identify bypass techniques and document their findings.
Membership Inference Defenses
Evaluating and implementing defenses against membership inference attacks that determine whether specific samples were in a model's training set.
Adaptive Guardrail Systems
Guardrails that dynamically adjust their sensitivity based on threat intelligence, user risk scoring, and behavioral patterns.
Constitutional Classifiers
Anthropic's Constitutional Classifiers defense: using constitutional AI principles to train input/output classifiers that withstood 3,000+ hours of adversarial red teaming.
Adversarial Training for LLM Defense
Use adversarial training techniques to improve LLM robustness against known attack patterns.
Agent Permission Model Design
Designing least-privilege permission models for LLM agents with tool access including capability-based security.
Agent Sandboxing Strategies
Sandboxing and isolation strategies for limiting the blast radius of compromised LLM agents.
The Attacker Moves Second Problem
Why static LLM defenses fail against adaptive adversaries: analysis of 12 bypassed defenses and implications for defense design.
Canary Tokens for Injection Detection
Implementing canary token systems that detect prompt injection by monitoring for canary leakage in model outputs.
Circuit Breaker Patterns for LLMs
Implement circuit breaker patterns that halt LLM processing when anomalous behavior is detected.
Confidential Computing for LLM Inference
Using trusted execution environments and confidential computing for secure LLM inference and data protection.
Constitutional AI as Defense Strategy
Using constitutional AI principles to build inherently safer LLM applications resistant to attacks.
Content Filtering Architecture
Designing content filtering systems for LLM applications covering input, output, and context filtering.
Data Loss Prevention for LLM Applications
DLP systems designed specifically for LLM applications to prevent sensitive data exposure through model outputs.
Defense-in-Depth for LLM Applications
Implementing layered defense architectures for production LLM applications.
Guardrails Framework Comparison 2025
Comparative analysis of NeMo Guardrails, LLM Guard, Rebuff, and custom guardrail implementations.
Human Oversight Integration Patterns
Patterns for integrating human oversight into AI workflows for safety-critical operations.
AI Incident Response Playbook (Defense Mitigation)
Detailed incident response playbook for AI-specific security incidents including detection, containment, and recovery.
Defense & Mitigation
Defensive strategies for AI systems including guardrails architecture, monitoring and observability, secure development practices, remediation mapping, and advanced defense techniques.
Input Validation Architecture for LLMs
Designing input validation pipelines that detect and neutralize prompt injection before reaching the model.
Instruction Hierarchy Enforcement
Techniques for enforcing instruction priority between system prompts, user inputs, and retrieved content.
LLM Firewall Architecture Patterns
Design patterns for implementing LLM firewalls that inspect and filter both input and output traffic to LLM applications.
LLM Honeypot Techniques
Deploying honeypot prompts and trap mechanisms to detect and characterize adversarial probing of LLM systems.
LLM Monitoring and Anomaly Detection
Building monitoring systems that detect adversarial usage patterns in LLM applications.
LLM Security Testing Automation
Building automated security testing pipelines for LLM applications using CI/CD integration and continuous scanning.
MCP Server Security Hardening
Hardening MCP server implementations against tool poisoning, transport attacks, and privilege escalation.
Multi-Model Consensus Defense
Using multiple models as cross-validators to detect adversarial manipulation through consensus disagreement.
Output Provenance Tracking
Systems for tracking the provenance of LLM outputs to detect hallucination, data leakage, and instruction injection.
Output Sanitization Patterns
Patterns for sanitizing LLM outputs to prevent information leakage and harmful content delivery.
Privilege Separation in LLM Applications
Implement privilege separation to limit the capabilities available to the LLM based on context and user role.
Prompt Injection Canary System
Deploy canary strings in system prompts to detect and alert on prompt injection and extraction attempts.
Training Prompt Injection Classifiers
Methodologies for training and evaluating ML classifiers that detect prompt injection attempts with high accuracy.
ML-Based Prompt Injection Detection
Building and training ML classifiers for real-time prompt injection detection.
RAG System Security Hardening
Comprehensive guide to hardening RAG systems against poisoning, injection, and data exfiltration.
Rate Limiting and Abuse Prevention
Implementing rate limiting and abuse prevention for LLM API endpoints and applications.
Red Team as a Service for AI Systems
Building and operating continuous AI red teaming services including engagement models, tooling, and reporting.
Red Team-Driven Defense Improvement
Using red team findings to systematically improve LLM application defenses.
Response Consistency Checking
Implement consistency checking between model responses and known facts to detect manipulation.
Runtime Model Behavior Monitoring
Real-time monitoring systems for detecting behavioral anomalies in deployed LLM applications.
Safety Layer Benchmarking Methodology
Standardized methodology for benchmarking the effectiveness of LLM safety layers against diverse attack categories.
Secure Development
Security-by-design principles for AI applications including defensive prompt engineering, input validation, output sanitization, and integrating security testing into CI/CD pipelines.
Secure Function Calling Design
Designing secure function calling interfaces that prevent unauthorized tool use and data exfiltration.
Secure RAG Pipeline Design Patterns
Security-first design patterns for RAG pipelines including source validation, content sanitization, and retrieval controls.
Semantic Similarity-Based Defense
Using embedding similarity analysis to detect adversarial inputs that are semantically close to known attack patterns.
Token Attribution Monitoring
Monitor token attributions in model outputs to detect adversarial influence on generation.
Tokenizer-Level Defense Mechanisms
Implementing security checks at the tokenizer level to detect and neutralize adversarial token patterns.
Adaptive Defense Systems for LLMs
Building defense systems that adapt to novel attack patterns using online learning and feedback loops.
Blue Team LLM Operations Guide
Operational guide for blue teams defending LLM applications including monitoring, triage, and response.
Canary Word Monitoring Systems
Deploying canary words in system prompts and documents to detect and alert on prompt injection and leakage.
Context Isolation Patterns for LLM Apps
Architectural patterns for isolating untrusted content from trusted instructions in LLM applications.
Data Loss Prevention for LLM Applications (Defense Mitigation)
Implementing DLP controls for LLM applications to prevent exfiltration of sensitive organizational data.
Defense Evaluation Methodology
Systematic methodology for evaluating the effectiveness of AI defenses against known attack categories.
Defense-in-Depth Reference Architecture
Complete reference architecture for defense-in-depth LLM application security with implementation blueprints.
Function Calling Authorization Framework
Building fine-grained authorization frameworks for function calling that enforce capability-based security.
LLM Firewall Architecture Design
Designing and implementing an LLM firewall that inspects, filters, and logs all model interactions.
Multi-Model Safety Validation Architecture
Using multiple models to cross-validate inputs and outputs for safety in a mutually-checking architecture.
Multi-Tenant Isolation for LLM Services
Implementing strong tenant isolation in multi-tenant LLM services to prevent cross-tenant attacks.
Output Watermarking as Defense
Using output watermarking for content provenance tracking and misuse detection in LLM applications.
Privacy-Preserving Inference Patterns
Implementing privacy-preserving inference patterns to protect sensitive data processed by LLM applications.
Real-Time Safety Monitoring Systems
Building real-time safety monitoring and alerting systems for deployed LLM applications.
Rollback-Safe AI Model Deployment
Implementing safe deployment practices with automatic rollback triggers for AI model security regressions.
Secure MCP Deployment Patterns
Deployment patterns for securing MCP server implementations in production agent environments.
Semantic Similarity-Based Defense (Defense Mitigation)
Using semantic similarity to detect prompt injection by comparing user inputs against known attack patterns.
System Prompt Hardening Patterns
Design patterns for creating injection-resistant system prompts with defense-in-depth prompt architecture.
Token-Level Anomaly Detection
Building token-level anomaly detection systems that identify adversarial patterns in input sequences.
User Intent Classification for Safety
Building user intent classifiers that distinguish legitimate requests from adversarial manipulation attempts.
Zero Trust Architecture for LLM Applications
Applying zero trust principles to LLM application architectures including identity, device, and data plane security.
AI Defense Taxonomy
A comprehensive categorization of all AI defense approaches organized by layer, method, and effectiveness, providing a structured framework for evaluating defense strategies.
Defense and Military AI Security
Security implications of AI in defense applications including autonomous systems, intelligence analysis, and decision support.
Defense and Military AI Security (Industry Verticals)
Security analysis of AI in defense applications including autonomous systems, intelligence, and decision support.
Defense-Informed Injection Design
Methodology for designing injections that account for known defensive mechanisms.
Injection Detection Research
State-of-the-art research in injection detection including perplexity-based methods, classifier approaches, and ensemble techniques.
Defense vs Red Team War Game
Alternate between attacking and defending an LLM application to develop skills in both offensive and defensive operations.
Lab: Build Your First Defense
Hands-on lab for building a basic AI input defense system using keyword matching, regex filters, and simple classifiers to block prompt injection attempts.
Basic Defense Mechanism Testing
Identify and categorize the defensive mechanisms present in a target LLM application through structured probing.
Lab: Build Your First Defense (Beginner Lab)
Create a simple input filter that blocks common prompt injection patterns, then test it against the attack techniques you have learned in previous labs.
Lab: Injection Detection Tool
Build a basic prompt injection detection tool using pattern matching, heuristics, and LLM-based classification to identify malicious inputs before they reach the target model.
Defense Destroyer Challenge
Bypass a state-of-the-art multi-layer defense system to extract a protected secret from the model.
Defense Gauntlet: Level 2 — Layered Security
Bypass a layered defense system with input classification, guardrails, and output filtering.
CTF: Defense Gauntlet (Blue Team)
Blue team CTF challenge where you build and defend an AI chatbot against a series of increasingly sophisticated automated attacks.
Defense Builder: Construct and Survive
Build defensive guardrails for an LLM application and survive 100 automated attack rounds.
Lab: Defense Effectiveness Measurement
Hands-on lab for quantifying AI guardrail robustness using attack success rates, evasion metrics, false positive rates, and statistical analysis of defense performance.
Lab: Defense Regression Testing Setup
Build a regression testing framework to continuously verify that LLM defenses remain effective against known attack patterns.
Simulation: Build & Defend a Chatbot
Defense simulation where you build a chatbot with layered defenses, test it against a standardized attack suite, measure defense effectiveness, and iterate on weaknesses.
Simulation: Defense in Depth
Expert-level defense simulation implementing a full defense stack including input filter, output monitor, rate limiter, anomaly detector, and circuit breaker, then measuring effectiveness against automated attacks.
Simulation: Guardrail Engineering
Defense simulation where you design and implement a multi-layer guardrail system, test it against progressively sophisticated attacks, and document false positive/negative rates.
Simulation: AI SOC Simulation
Defense simulation where you set up monitoring for an AI application, then respond to simulated attacks by practicing alert triage, investigation, and escalation procedures.
Multimodal Defense Strategies
Comprehensive defense approaches for multimodal AI systems: cross-modal verification, perceptual hashing, NSFW detection, input sanitization, and defense-in-depth architectures.
Defending Multimodal AI Systems
Comprehensive defense strategies for multimodal AI systems including input sanitization, cross-modal safety classifiers, instruction hierarchy, and monitoring for adversarial multimodal inputs.
Defense Mechanism Catalog
Catalog of defensive mechanisms with effectiveness ratings against each attack category.
Defense Mechanism Comparison
Comprehensive comparison of LLM defense mechanisms including guardrails, classifiers, filtering, and architectural approaches with effectiveness data.
LLM Security Checklist
Comprehensive security checklist for LLM-powered applications covering input validation, prompt hardening, output filtering, tool security, RAG pipelines, and incident response.
Purple Teaming for AI
Collaborative attack-defense exercises for AI systems: structuring purple team engagements, real-time knowledge transfer, joint attack simulation, and measuring defensive improvement through iterative testing.
Adversarial Robustness Testing Framework
Build a framework for continuously testing adversarial robustness of deployed LLM defense mechanisms.
Adversarial Training for LLM Defense (Defense Walkthrough)
Implement adversarial training techniques to improve LLM robustness against prompt injection and jailbreaking.
Agent Permission Boundary Enforcement
Implement fine-grained permission boundaries for LLM agents that limit tool access based on context and user role.
Automated Defense Regression Testing
Build automated regression tests for LLM security defenses using attack replay and continuous monitoring.
Automated Red Team Defense Loop
Build an automated continuous red team testing loop that discovers and patches vulnerabilities iteratively.
Behavioral Anomaly Detection for LLMs
Implement behavioral anomaly detection that identifies when model outputs deviate from expected safety profiles.
Building an Input Safety Classifier
Build a production-quality input classifier that detects prompt injection attempts.
Building a Production Input Sanitizer
Step-by-step walkthrough for building a production-grade input sanitizer that cleans, normalizes, and validates user prompts before they reach an LLM, covering encoding normalization, injection pattern stripping, length enforcement, and integration testing.
Canary Token Deployment
Step-by-step walkthrough for deploying canary tokens in LLM system prompts and context to detect prompt injection and data exfiltration attempts, covering token generation, placement strategies, monitoring, and alerting.
Capability-Based Access Control
Step-by-step walkthrough for implementing fine-grained capability controls for LLM features, covering capability token design, permission scoping, dynamic capability grants, and audit trails.
Constitutional AI Implementation Guide
Implement constitutional AI principles in a custom fine-tuning and RLHF pipeline.
Constitutional Classifier Setup
Step-by-step walkthrough for implementing constitutional AI-style classifiers that evaluate LLM outputs against a set of principles, covering principle definition, classifier training, chain-of-thought evaluation, and deployment.
Setting Up Content Filtering
Step-by-step walkthrough for implementing multi-layer content filtering for AI applications: keyword filtering, classifier-based detection, LLM-as-judge evaluation, testing effectiveness, and tuning for production.
Context Isolation Pattern Implementation
Implement context isolation patterns that prevent instruction leakage between system prompts and user data.
Data Loss Prevention for LLM Apps
Build a data loss prevention layer for LLM applications that prevents sensitive data exposure in outputs.
Defense Benchmarking System
Build a benchmarking system to continuously evaluate defense effectiveness against known attack classes.
Defense-in-Depth Architecture for LLM Apps
Design and implement a complete defense-in-depth architecture for production LLM applications.
Deploying NeMo Guardrails
Step-by-step walkthrough for setting up NVIDIA NeMo Guardrails in production, covering installation, Colang configuration, custom actions, topical and safety rails, testing, and monitoring.
Dual LLM Architecture Setup
Step-by-step walkthrough for implementing a dual LLM pattern where one model generates responses and a second model validates them, covering architecture design, validator prompt engineering, latency optimization, and failure handling.
Hallucination Detection
Step-by-step walkthrough for detecting and flagging hallucinated content in LLM outputs, covering factual grounding checks, self-consistency verification, source attribution validation, and confidence scoring.
Building Input Guardrails for LLM Applications
Step-by-step walkthrough for implementing production-grade input guardrails that protect LLM applications from prompt injection, content policy violations, and resource abuse through multi-layer validation, classification, and rate limiting.
Incident Response Playbook for AI Security Breaches
Walkthrough for building an incident response playbook tailored to AI security breaches, covering detection triggers, triage procedures, containment strategies, investigation workflows, remediation validation, and post-incident review processes.
AI Incident Response System Setup
Set up comprehensive incident response capabilities for AI-specific security incidents.
Defense Implementation Walkthroughs
Step-by-step guides for implementing AI security defenses: guardrail configuration, monitoring and detection setup, and incident response preparation for AI systems.
Input Embedding Firewall Deployment
Deploy an embedding-based firewall that detects injection attempts through semantic similarity to known attack patterns.
Secure Input/Output Logging for Defense
Implement secure logging for LLM input/output pairs that supports forensic analysis without exposing sensitive data.
Instruction Hierarchy Enforcement (Defense Walkthrough)
Step-by-step walkthrough for enforcing instruction priority in LLM applications, ensuring system-level instructions always take precedence over user inputs through privilege separation, instruction tagging, and validation layers.
LLM Firewall Architecture Design (Defense Walkthrough)
Design and implement a comprehensive LLM firewall with input analysis, output filtering, and behavioral monitoring.
LLM Honeypot Deployment
Deploy LLM honeypots to detect and study attacker behavior patterns and techniques.
LLM Judge Implementation
Step-by-step walkthrough for using an LLM to judge another LLM's outputs for safety and quality, covering judge prompt design, scoring rubrics, calibration, cost optimization, and deployment patterns.
Model Behavior Monitoring Setup
Set up comprehensive model behavior monitoring to detect drift, anomalies, and potential compromise.
Validating and Sanitizing Model Outputs
Walkthrough for building output validation systems that verify LLM responses meet structural, factual, and safety requirements before delivery, covering schema validation, factual grounding checks, response consistency verification, and safe rendering.
Monitoring LLM Applications for Abuse
Build a monitoring and alerting system to detect ongoing attacks against LLM applications.
Production Monitoring for LLM Security Events
Walkthrough for building production monitoring systems that detect LLM security events in real time, covering log collection, anomaly detection, alert configuration, dashboard design, and incident correlation.
Multi-Layer Input Validation
Step-by-step walkthrough for building a defense-in-depth input validation pipeline that combines regex matching, semantic similarity, ML classification, and rate limiting into a unified validation system for LLM applications.
Multi-Model Safety Consensus
Implement safety consensus mechanisms where multiple models must agree before executing sensitive actions.
Output Content Classifier
Step-by-step walkthrough for building a classifier to filter harmful LLM outputs, covering taxonomy definition, multi-label classification, threshold calibration, and deployment as a real-time output gate.
Output Filtering Pipeline Design
Design and implement a multi-stage output filtering pipeline for LLM applications.
Output Filtering and Content Safety Implementation
Walkthrough for building output filtering systems that inspect and sanitize LLM responses before they reach users, covering content classifiers, PII detection, response validation, canary tokens, and filter bypass resistance.
Output Grounding and Verification
Implement output grounding verification to ensure LLM responses are factually supported by provided context.
PII Redaction Pipeline
Step-by-step walkthrough for building an automated PII detection and redaction pipeline for LLM outputs, covering regex-based detection, NER-based detection, presidio integration, redaction strategies, and compliance testing.
Prompt Armor Implementation Guide
Implement a comprehensive prompt armoring system with instruction isolation, delimiter hardening, and priority enforcement.
Prompt Classifier Training
Step-by-step walkthrough for training a machine learning classifier to detect malicious prompts, covering dataset curation, feature engineering, model selection, training pipeline, evaluation, and deployment as a real-time detection service.
ML-Based Prompt Injection Detection Systems
Walkthrough for building and deploying ML-based prompt injection detection systems, covering training data collection, feature engineering, model architecture selection, threshold tuning, production deployment, and continuous improvement.
Prompt Injection Honeypot Setup
Deploy honeypot prompts and canary data that detect and characterize prompt injection attempts.
Implementing Access Control in RAG Pipelines
Walkthrough for building access control systems in RAG pipelines that enforce document-level permissions, prevent cross-user data leakage, filter retrieved context based on user authorization, and resist retrieval poisoning attacks.
RAG Input Sanitization Walkthrough
Implement input sanitization for RAG systems to prevent document-based injection attacks.
Rate Limiting and Abuse Prevention for LLM APIs
Walkthrough for implementing rate limiting and abuse prevention systems for LLM API endpoints, covering token bucket algorithms, per-user quotas, cost-based limiting, anomaly detection, and graduated enforcement.
AI Rate Limiting Walkthrough
Step-by-step walkthrough for implementing token-aware rate limiting for AI applications: request-level limiting, token budget enforcement, sliding window algorithms, abuse detection, and production deployment.
Real-Time Attack Detection System
Build a real-time attack detection system that monitors LLM interactions for adversarial patterns.
Red Team-Defense Feedback Loop
Build a continuous red team-defense improvement loop with automated testing and metric tracking.
Regex-Based Prompt Filter
Step-by-step walkthrough for building a regex-based prompt filter that detects common injection payloads using pattern matching, covering pattern library construction, performance optimization, false positive management, and continuous updates.
Response Boundary Enforcement
Step-by-step walkthrough for keeping LLM responses within defined topic, format, and content boundaries, covering boundary definition, violation detection, response rewriting, and boundary drift monitoring.
Response Watermarking Implementation
Implement response watermarking to enable traceability and detect unauthorized reproduction of LLM outputs.
Runtime Safety Monitor Implementation
Implement a runtime safety monitor that detects and blocks unsafe model outputs in real-time.
Training Custom Safety Classifiers
Train custom safety classifiers tuned to your application's specific threat model and content policy.
Sandboxed Tool Execution
Step-by-step walkthrough for running LLM tool calls in isolated sandboxes, covering container-based isolation, resource limits, network restrictions, and output sanitization.
Sandboxing and Permission Models for Tool-Using Agents
Walkthrough for implementing sandboxing and permission models that constrain tool-using LLM agents, covering least-privilege design, parameter validation, execution sandboxes, approval workflows, and audit logging.
Secure Agent Architecture Design
Design a secure architecture for LLM agent systems with sandboxing, capability controls, and audit trails.
Secure Function Calling Patterns
Implement secure function calling with input validation, output sanitization, and capability restrictions.
Secure RAG Architecture Walkthrough
Design and implement a secure RAG architecture with document sanitization, access controls, and output validation.
Secure RAG Pipeline Architecture
Build a secure RAG pipeline with document sanitization, retrieval validation, and injection-resistant augmentation.
Semantic Similarity Detection
Step-by-step walkthrough for using text embeddings to detect semantically similar prompt injection attempts, covering embedding model selection, vector database setup, similarity threshold tuning, and production deployment.
Session Isolation Patterns
Step-by-step walkthrough for isolating user sessions in LLM applications to prevent cross-contamination of context, memory, and permissions between users.
Structured Output Validation
Step-by-step walkthrough for validating structured LLM outputs against schemas, covering JSON schema validation, type coercion, constraint enforcement, and handling malformed model outputs gracefully.
System Prompt Protection Techniques
Implement multiple layers of system prompt protection against extraction, override, and leakage attacks.
AI Security Threat Intelligence
Build a threat intelligence pipeline for staying current with AI security threats and attack techniques.
Token-Level Input Filtering
Implement token-level input filtering that detects injection patterns at the tokenization stage before model processing.
Tool Call Authorization Framework
Implement a tool call authorization framework that validates tool invocations against policy before execution.
Toxicity Scoring Pipeline
Step-by-step walkthrough for building a toxicity scoring pipeline for LLM output filtering, covering model selection, multi-dimensional scoring, threshold calibration, and production deployment with real-time scoring.
Unicode Normalization Defense
Step-by-step walkthrough for implementing Unicode normalization to prevent encoding-based prompt injection bypasses, covering homoglyph detection, invisible character stripping, bidirectional text handling, and normalization testing.
Agent Tool Access Control Implementation
Implement fine-grained tool access control for LLM agents with capability-based security and approval workflows.
Anomaly Detection for LLM Traffic
Build anomaly detection for LLM API traffic to identify attack patterns, abuse, and prompt injection attempts.
Conversation Integrity Monitoring
Build a conversation integrity monitoring system that detects manipulation across multi-turn interactions.
Defense Effectiveness Metrics Dashboard
Build a dashboard to measure and visualize the effectiveness of defensive measures against attack categories.
Automated Defense Testing Pipeline
Build an automated pipeline that continuously tests defensive measures against evolving attack techniques.
Embedding Poisoning Detection System
Build a detection system for identifying poisoned documents in vector databases using statistical analysis.
Function Calling Guardrails Implementation
Implement guardrails for function calling that validate tool selection, parameters, and execution scope.
LLM Honeypot Deployment Guide
Deploy LLM-based honeypots to detect, analyze, and learn from attacker techniques in production environments.
Incident Response Playbook for LLM Applications
Design and implement an incident response playbook specific to LLM application security incidents.
LLM Guard Production Deployment Guide
Deploy LLM Guard in a production environment with custom scanners, performance optimization, and monitoring.
MCP Server Security Hardening Guide
Harden MCP server implementations against tool poisoning, transport attacks, and capability escalation.
Multi-Model Defense Ensemble
Build an ensemble defense system using multiple models to cross-validate inputs and outputs for safety.
NeMo Guardrails Advanced Configuration
Advanced NeMo Guardrails configuration including custom actions, knowledge base integration, and multi-model pipelines.
Building an Output Scanning Pipeline
Build a production output scanning pipeline that detects data leakage, harmful content, and injection artifacts.
PII Detection and Redaction for LLMs
Build a PII detection and redaction system for LLM inputs and outputs to prevent data exposure.
Training a Prompt Injection Classifier
Train a custom prompt injection detection classifier using labeled datasets and modern NLP techniques.
RAG Document Sandboxing Implementation
Implement document-level sandboxing for RAG systems to prevent cross-document injection and privilege escalation.
Adaptive Rate Limiting for LLM APIs
Implement adaptive rate limiting that adjusts thresholds based on detected attack patterns and user behavior.
Secure RAG Architecture Implementation
Implement a security-hardened RAG architecture with input sanitization, access control, and output validation.
System Prompt Protection Layers
Implement multi-layer system prompt protection including injection-resistant formatting and runtime validation.
Zero Trust Architecture for LLM Apps
Implement zero trust principles in LLM application architecture with continuous verification and least privilege.
Walkthroughs
Step-by-step guided walkthroughs covering red team tools, engagement methodology, defense implementation, platform-specific testing, and full engagement workflows.
LLM Guard Deployment and Testing
Deploy LLM Guard for input/output scanning and test its effectiveness against common attacks.
Defense Benchmarking Tool Development
Build a tool for benchmarking the effectiveness of defensive measures against standardized attack suites.