Technical guides and threat analysis for teams building and securing autonomous AI systems.
How to integrate AI agent security testing into CI/CD pipelines — gating deployments on adversarial AI testing results, running FortifAI in GitHub Actions, and building continuous AI security coverage.
RAG pipelines introduce unique data leakage risks — poisoned retrieval, cross-user context contamination, and indirect prompt injection. This guide covers how to test RAG systems for data leakage vulnerabilities.
How to security test LangChain agents for prompt injection, tool abuse, and data leakage. A practical guide for LangChain developers covering vulnerability assessment, adversarial testing, and hardening.
A practical AI red teaming methodology for autonomous LLM agents — covering threat modeling, attack simulation, multi-agent testing, and how to build a continuous red team program for AI systems.
Behavioral AI testing monitors how LLM agents respond under adversarial conditions — detecting reasoning deviations, unexpected tool calls, and goal drift that signature-based detection misses.
How to perform a comprehensive AI agent vulnerability assessment — covering threat modeling, adversarial testing, OWASP Agentic Top 10 coverage, and CI/CD integration for continuous security.
The most critical AI agent security risks in 2026 — from prompt injection and RAG poisoning to multi-agent privilege escalation and supply chain attacks. What's changed and what your team needs to test for.
AI agents can exfiltrate credentials, PII, and proprietary data through prompt injection, tool abuse, and RAG poisoning. This guide covers the most common data leakage vectors and how to detect them.
A complete technical guide to the OWASP Agentic Top 10 — the definitive threat taxonomy for autonomous AI agents. Learn what each risk means, how attacks happen, and how runtime defenses work.
Prompt injection is the most exploited vulnerability in AI agent systems. This guide explains direct and indirect injection attacks with real-world examples, and covers runtime defenses that actually work.
Zero-trust is the right security model for autonomous AI agents — but traditional zero-trust frameworks weren't designed for agentic systems. Here's how to apply zero-trust principles to agent identity, tool access, and memory in production.