AI and LLM security testing guides

Agentic AI security explained: the OWASP agentic Top 10, real agent incidents, a 12-point test plan, and fixed-price AI testing from $4,500.

Claude Code and MCP vulnerabilities from October 2026, plus the Claude Agent SDK and Langflow flaws: affected versions, fixes and how to test your AI agents.

LLMjacking in October 2026: how AI keys and cloud credentials were stolen via Railway, Zeabur, LiteLLM and other AI tools, and what to rotate now.

An MCP server security guide: the risks in Model Context Protocol servers, what the spec requires, and a test plan for tool poisoning, auth and SSRF.

AI is changing penetration testing, but it will not replace human testers. Here is what it does well, where it falls short, and why judgment still wins.

Indirect prompt injection hides attacker instructions in content an AI later reads. Learn how the attack works, why it is dangerous, and how to defend.

AI startups fold, get acquired, and pivot constantly. If your product depends on one, here is how to stay resilient when your AI provider disappears or changes.

An LLM becomes far more dangerous the moment you connect it to tools and data. Here is how integrations expand the attack surface, and how to contain the risk.

A plain-English guide to the OWASP Top 10 for LLM Applications: what each risk means, why it matters, and how to test your AI system against it.

A plain-English glossary of adversarial machine learning: evasion, poisoning, model inversion, extraction, and the other terms security teams need to know.

Lock an AI assistant down too hard and it becomes useless; too loose and it becomes a liability. Here is how to find the balance between security and usability.