giskard-oss

面向智能体系统的评估、红队测试与测试生成 模块化、轻量级、动态且异步优先 文档 • 官网 • 社区 !IMPORTANT Giskard v3 是一次全新重写，专为 AI 智能体的动态、多轮测试而设计。此版本移除了重型依赖以提升效率，同时引入了更强大的 AI 漏洞扫描器和增强的 RAG 评估——两者现在均原生集成于 giskard-scan，不再依赖 v2。仅针对表格/ML 模型 的旧版扫描仍仅支持 v2。 Giskard v2 仍然可用，但已不再积极维护。 关注进展 → 阅读 v3 公告 · 路线图 安装 root@kitploit: pip install giskard 检查（+...

ws4-secure-design-agentic-systems

CoSAI 워크스트림 4: 에이전트 시스템을 위한 보안 설계 패턴 이 저장소는 CoSAI 워크스트림 4, 에이전트 시스템을 위한 보안 설계 패턴의 작업을 위한 것입니다. CoSAI는 OASIS 오픈 프로젝트이며, 업계 선도 조직의 AI 및 보안 전문가들로 구성된 개방형 생태계로서 안전한 AI 배포를 위한 모범 사례를 공유하고 AI 보안 연구 및 제품 개발에 협력하는 데 전념합니다. CoSAI에 대한 자세한 내용은 CoSAI 웹사이트와 거버넌스, 후원사, 명단 및 프로젝트 헌장에 관한 정보가 있는 오픈 프로젝트 저장소를...

Red-Teaming the Agentic Red-Team

The use of agentic systems to perform offensive security operations has moved from a theoretical possibility to a commoditized capability. However, while the community has focused on creating more and more capable agents, less attention has been allocated to assessing the security of those system...

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...

poc-factory-sample-output

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...