preambleai/prompt-injector
AI security and prompt injection payload toolkit
AI 安全相关的开源项目、工具与仓库动态,按内容类型筛选,支持搜索。
AI security and prompt injection payload toolkit
🛡 The reference playbook for pentesting AI chatbots & LLM-powered apps in one place. Ready-to-use payloads covering the full OWASP LLM Top 10 plus frontier vectors (MCP · RAG · A2A · computer-use · voice)
Multi-tier firewall for AI agents — blocks prompt injections, jailbreaks, and scope violations. Local tiers first; LLM judge only when uncertain.
GitHub 仓库 togg53192-cmd/jailbreaks 收录 32 条针对 26+ 模型的完整提示词,涵盖 OpenCode Zen 免费隐身模型(LongCat 2.5。
ECC 2.2.3 是 MIT 许可的开源编码 Agent 工程系统,通过 `npx ecc-universal@2.2.3 setup` 为 Claude Code、Codex、Kimi Code 提供引导式安装,原生插件 slug 为 `ecc@ecc`。
nightly-labs 推出 OpenBot,一款本地优先的桌面工作区,为每个 AI 代理提供独立工作区与会话,支持本地 Codex App Server、Claude Code,并通过 ACP 接入 Grok CLI、OpenCode、Gemini、Cursor CLI 和 Cline CLI。
AI red-teaming tool and LLM security framework to evaluate agentic AI applications. Tests prompt injections, handles vulnerability assessment, SBOM generation, and static analysis.
Provides a collection of sandboxes, exploitation code, and tutorials that exemplifies GenAI Red Teaming exercises. It aims to help security researchers and developers test, probe, and evaluate the safety and security of GenAI-based applications.
A collection of skills, agents, commands, and workflows for security researchers. Compatible with Claude Code, Claude Desktop, OpenCode, and other AI coding tools.
Open-source runtime AI agent security tool - monitors and controls AI agents, catching malicious tool use, prompt injection, and policy drift in real time, before the agent acts.
A curation of awesome tools, documents and projects about LLM Security.
AI/ML and Generative AI Security Assessment Framework for AWS. Automatically audit Amazon Bedrock , SageMaker ,AgentCore, and Agent Registry workloads for security best practices
AWS 在 GitHub 开源 Agent Toolkit for AWS,为 Claude Code、Codex、Cursor、Devin、Kiro、fx 等编码 Agent 提供 MCP Server 配置。
Ponytail 5 是面向 AI 编程智能体的技能提示词,在 Claude Code 中用同一智能体对比测试 39 个任务(含真实 FastAPI + React 仓库、Opus 5.5、各跑 5 次),代码量减少 53%、耗时减少 41%、成本降低 26%、token 减少 45%。