AI Security Hub
If you want the site to lead with AI security, this is the commercial and SEO cluster to build around: agent misuse, LLM data exposure, identity, tooling, and governance.
Guides
28
Latest Update
May 8, 2026
Primary Intent
A focused collection of SecureCodeReviews guides on prompt injection, AI agents, governance, MCP, and enterprise LLM risk reduction.
AI Security Testing Tools: Garak, PyRIT, promptfoo, and the Controls They Actually Validate
A practical guide to AI security testing tools for LLM and agentic applications. Explains what Garak, PyRIT, and promptfoo are good at, where each tool falls short, and how to combine automated testing with human review for prompt injection, data leakage, and unsafe tool use.
AI Agent Memory Security: Context Poisoning, Secret Retention, and Session Isolation
Agent memory is one of the fastest ways an AI assistant turns one bad interaction into a recurring security problem. Learn how context poisoning works, where secret retention happens, and how to design memory systems that do not become persistent attack surface.
LLM Guardrails in Production: Filters, Policy Engines, and Failure Modes
Guardrails are not a checkbox. This guide explains how real production guardrails work, where they fail, and how to combine prompt attack detection, output controls, and fallback behavior into something operators can actually trust.
Model Provenance Security: How to Verify Open-Weight Models Before Deployment
A model file is a software artifact, not a neutral blob. Learn how to verify open-weight models, reduce pickle risk, use safer weight formats, and build provenance checks into your AI deployment pipeline.
Secure Tool Calling for LLMs: Function Calling Risks and Runtime Controls
Tool calling is where an LLM application stops being a text system and starts becoming an action system. Learn the runtime controls, permission boundaries, and confirmation patterns that keep function calling from becoming an automation incident.
Multi-Tenant LLM Security: Preventing Cross-Tenant Data Leakage in Shared AI Apps
Shared AI platforms fail at the boundaries first. Learn how cross-tenant data leakage happens in prompts, caches, retrieval, and logs, and how to design tenant isolation that still holds when the AI features become more complex.
Self-Hosted LLM Security: Hardening vLLM, TGI, Ollama, and Inference APIs
Self-hosting an LLM gives you more control, but it also moves model, runtime, and network risk onto your team. This guide covers the hardening steps that matter for inference servers, private model pulls, prompt logs, and exposed GPU infrastructure.
AI Data Leakage Prevention: Prompts, Logs, Outputs, and Enterprise Controls
Sensitive data leaks in AI systems rarely come from one place. They move through prompts, retrieval context, outputs, logs, and evaluation traces. This guide shows how to build AI DLP controls that actually match how LLM apps are used in production.
Fine-Tuning Security: Poisoned Datasets, LoRA Risks, and Safer Training Pipelines
Fine-tuning moves AI risk into your own pipeline. Learn how dataset poisoning, unsafe adapters, and weak evaluation practices affect fine-tuned models, and how to secure training workflows without grinding delivery to a halt.
LLM Gateway Security: Model Routing, Budget Controls, and Abuse Detection
An LLM gateway is not just a cost-control layer. It is the place where authentication, model policy, rate limiting, prompt controls, and provider failover need to come together. Learn how to design gateway security that does more than forward requests.
AI Evals Security: How to Test LLM Applications Without Gaming Your Benchmarks
Evaluation pipelines decide what gets shipped, but they are often easier to game than teams admit. Learn how to secure AI evals against leakage, benchmark contamination, weak security coverage, and unsafe auto-promotion rules.
Prompt Injection Attacks: Complete Prevention Guide for 2026
The most comprehensive guide to prompt injection attacks — direct, indirect, and multi-turn. Covers real-world breaches, OWASP mitigations, and defense-in-depth strategies with code examples for securing LLM applications in production.
RAG Security: Vulnerabilities in Retrieval-Augmented Generation Systems (2026)
Deep dive into security vulnerabilities in RAG (Retrieval-Augmented Generation) pipelines — data poisoning, indirect prompt injection via retrieved context, embedding inversion attacks, and tenant isolation failures. Includes real-world breaches and production-ready defenses.
AI Supply Chain Security: Pre-trained Models, Datasets & ML Pipeline Risks (2026)
Your AI is only as secure as its supply chain. This guide covers backdoored model weights on Hugging Face, poisoned training datasets, compromised ML libraries, and the emerging AI SBOM standard — with real incidents and production defenses.
LLM Output Security: Preventing XSS, Code Injection & Data Leakage in AI Apps (2026)
LLM output is untrusted input. This guide covers how AI-generated responses can introduce XSS, SQL injection, command injection, and data leakage — with production code examples for output sanitization, CSP headers, and structured output schemas.
AI Red Teaming: How to Test LLM Applications for Security Vulnerabilities (2026)
A practical, step-by-step methodology for red teaming LLM applications — from reconnaissance and prompt injection testing to output abuse and agentic AI exploitation. Includes 30+ test cases, open-source tools (Garak, PyRIT), and a scoring framework.
MCP Server Security: Why Model Context Protocol Is the Next Big Attack Surface
MCP connects AI agents to your tools, databases, and APIs. Here's why it's a massive security risk — and how to lock it down before attackers figure it out.
AI Security: Complete Guide to LLM Vulnerabilities, Attacks & Defense Strategies 2025
Master AI and LLM security with comprehensive coverage of prompt injection, jailbreaks, adversarial attacks, data poisoning, model extraction, and enterprise-grade defense strategies for ChatGPT, Claude, and LLaMA.
OWASP Top 10 for Agentic AI 2026: Complete Security Guide
The definitive guide to the OWASP Top 10 for Agentic AI Applications — a brand-new framework released December 2025. Covers goal hijacking, tool manipulation, prompt injection, and 7 more critical agentic AI risks with real-world case studies and mitigations.
How to Secure AI Agents: Identity & Access Management for Agentic AI
Machine identities now outnumber human identities 45:1. Learn how to implement IAM for AI agents — authentication, authorization, credential management, and delegation chains in multi-agent systems.
AI-Powered Attacks in 2026: Deepfakes, Vibe Coding & Automated Exploits
AI is supercharging cyberattacks. From $25M deepfake fraud to insecure AI-generated 'vibe code' to fully automated exploit chains, this guide covers the threats defenders face in 2026 with real cases, statistics, and defensive strategies.
Securing Generative AI APIs: MCP Security & Shadow AI Risks in 2026
Model Context Protocol (MCP) is the emerging standard for connecting AI to tools and data. But MCP servers, shadow AI usage, and AI supply chain attacks introduce critical risks. Learn how to secure generative AI APIs.
AI Governance Framework 2026: Building Guardrails for Enterprise AI
94% of executives say AI is the biggest driver of change, but only 44% have AI governance policies. This guide provides a complete AI governance framework with policy templates, risk assessment matrices, EU AI Act compliance, and organizational structure.
API Security Trends 2026: Protecting REST, GraphQL & gRPC in an AI-Driven World
APIs now account for 83% of web traffic. This guide covers the most critical API security trends for 2026 — AI-generated API abuse, GraphQL-specific attacks, gRPC security, API gateways, and runtime protection strategies.
API Security for AI Agents: Securing MCP, Function Calling & Tool Use
AI agents are the new API consumers. This guide covers securing APIs against AI-driven abuse — MCP server hardening, function calling guardrails, tool delegation authorization, and protecting sensitive endpoints from autonomous agents.
Securing RAG Pipelines: Retrieval-Augmented Generation Threats & Defenses
RAG is the most popular LLM architecture pattern — and the most attacked. Learn about document poisoning, embedding manipulation, and how to build secure RAG systems.
AI Red Teaming: How to Break LLMs Before Attackers Do
A practical guide to AI red teaming — adversarial testing of LLMs, prompt injection techniques, jailbreaking methodologies, and building an AI security testing program.
AI Security & LLM Threats: Prompt Injection, Data Poisoning & Beyond
A comprehensive analysis of AI/ML security risks including prompt injection, training data poisoning, model theft, and the OWASP Top 10 for LLM Applications. With practical defenses and real-world examples.