Updating the taxonomy of failure modes in agentic AI systems: What a year of red teaming taught us

In this article 1. Why the Taxonomy Needed Updating 2. Seven new failure modes 3. Operational findings: What red teaming showed 4. New mitigations 5. What to do this quarter When the Microsoft AI Red Team published the Taxonomy of Failure Modes in Agentic AI Systems in April 2025, the goal was a...

Defense in depth for autonomous AI agents

Designing Secure Autonomous AI Agents with Defense in Depth AI agents are moving beyond assistance and into action. Instead of generating content, they invoke tools, modify data, trigger workflows, and operate across systems with increasing autonomy. This shift changes the security problem...

Clawed and Dangerous: Can We Trust Open Agentic Systems?

Open agentic systems combine LLM-based planning with external capabilities, persistent memory, and privileged execution. They are used in coding assistants, browser copilots, and enterprise automation. OpenClaw is a visible instance of this broader class. Without much attention yet, their securit...

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Prompt Injection Guardrails Introduction In the rapidly e...

Toward Trustworthy Agentic AI: A Multimodal Framework for Preventing Prompt Injection Attacks

Powerful autonomous systems, which reason, plan, and converse using and between numerous tools and agents, are made possible by Large Language Models LLMs, Vision-Language Models VLMs, and new agentic AI systems, like LangChain and GraphChain. Nevertheless, this agentic environment increases the...

A Safety and Security Framework for Real-World Agentic Systems

This paper introduces a dynamic and actionable framework for securing agentic AI systems in enterprise deployment. We contend that safety and security are not merely fixed attributes of individual models but also emergent properties arising from the dynamic interactions among models, orchestrator...

Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges

Agentic AI systems powered by large language models LLMs and endowed with planning, tool use, memory, and autonomy, are emerging as powerful, flexible platforms for automation. Their ability to autonomously execute tasks across web, software, and physical environments creates new and amplified...

From Prompts to Protocols: How Agentic Systems, MCP, Vibe Coding, and Schema-Aware Tools Are Rewiring Software Engineering

Modern software engineering faces growing complexity across codebases, environments, and workflows. Traditional tools, although effective, rely heavily on…...

Building Effective Agents with Spring AI (Part 1)

In a recent research publication: Building effective agents, Anthropic shared valuable insights about building effective Large Language Model LLM agents. What makes this research particularly interesting is its emphasis on simplicity and composability over complex frameworks. Let's explore how...