Promptfoo
CLI tool that combines LLM prompt testing with red-teaming.
AI safety evaluation, red-teaming, LLM guardrails, vulnerability scanning, and compliance audit tools
CLI tool that combines LLM prompt testing with red-teaming.
Test and evaluate LLM prompts, agents, and RAG pipelines. Built-in red teaming and security evaluation for reliable AI applications.
SWE-agent takes a GitHub issue and automatically generates fixes using your LLM of choice, also applicable to cybersecurity auditing and competitive coding. NeurIPS 2024 paper.
754 structured cybersecurity skills for AI agents mapped to 5 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND and NIST AI RMF. Works with Claude Code, Codex CLI, Cursor, Gemini CLI and 20+ platforms.
OpenAI's framework for evaluating LLMs and LLM systems, providing an open-source registry of benchmarks and tools for systematic model assessment.
Fully autonomous AI Agents system capable of performing complex penetration testing tasks using multi-agent architecture with support for multiple LLM providers.
An automated penetration testing agentic framework powered by large language models for security testing and vulnerability discovery.
E2B provides secure cloud sandboxes for AI agents, supporting code execution, file operations, and isolated compute as an execution layer for coding and automation workflows.
Portkey AI Gateway is a blazing fast AI gateway with integrated guardrails, routing to 200+ LLMs with 50+ AI guardrails through a single fast and friendly API.
OpenSandbox is an open-source, secure, fast, and extensible sandbox runtime for AI agents, developed by Alibaba.
HexStrike AI is an advanced MCP server that lets AI agents autonomously run 150+ cybersecurity tools for automated pentesting, vulnerability discovery, and security research.
Microsoft's open-source context-aware PII detection and de-identification SDK for text, images, and structured data, providing sensitive data protection for LLM applications and agents.
NVIDIA's SkillSpector inspects and evaluates the tool-use and function-calling skills of LLM agents against safety, correctness, and performance criteria.
MCP server for Ghidra reverse engineering platform, enabling AI agents to autonomously perform binary analysis and vulnerability discovery.
Alias Robotics' open-source AI security research agent framework for multi-agent orchestration of cybersecurity tasks, integrating 300+ AI models, designed for red-team operations and security research.
NVIDIA's open-source LLM vulnerability scanner that automatically detects security issues in language models including safety vulnerabilities, hallucination tendencies, jailbreak risks, and prompt injection attacks.
OpenShell is the safe, private runtime for autonomous AI agents, developed by NVIDIA. Provides controlled execution environments and resource management.
Guardrails AI adds programmable guardrails to large language models, ensuring reliability and safety through input/output validation, structured data extraction, and custom validators.
Open-source library for structured validation and safety guardrails on LLM outputs.
Secure, local, cross-platform and programmable sandboxes for AI agents. Provides strict resource isolation using microVM technology.
Superagent protects AI applications against prompt injections, data leaks, and harmful outputs, embedding safety directly into your app.
NVIDIA NeMo Guardrails is an open-source toolkit for adding programmable guardrails to LLM-based conversational systems, supporting topic control, safety enforcement, and dialog guidance.
NVIDIA's LLM conversational guardrails framework with programmable safety boundaries.
An open-source evaluation and testing library for LLM agents providing automated model scanning, bias detection, performance benchmarking, and compliance checks.
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Most teams evaluate agents by checking a few examples. Real evaluation needs layered metrics, non-rotting datasets, and judges that push back. This article provides runnable code patterns and a practical decision framework.
Five-layer defense plus red-team loop, built on five open-source projects you can copy.
A systematic walkthrough of three major attack surfaces in AI agents, with practical code examples for prompt injection defense, tool permission scoping, and output filtering.
A deep architectural comparison of seven open-source coding agents across three paradigms — CLI-first, IDE-integrated, and fully autonomous — examining context management, tool access, and autonomy levels to help you pick the right tool for each development scenario.
Comparing container, WebAssembly, and process-level isolation approaches, with practical code for safely executing agent-generated code.
Four LLM gateways compared, with production patterns for fallback, smart routing, cost observability, and scheduling.