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AI Agent security testing framework with 160+ attack cases from latest research

Security regression testing for LLM apps, RAG pipelines and agents: find, verify, regress.

Security moat for AI agents. Runtime protection against prompt injection, tool misuse, and data exfiltration.

On-device DLP security for AI prompts and file uploads. Scan, mask, warn, or block sensitive data before it reaches AI tools.

Security middleware for Python LLM apps and services. Blocks prompt injection, masks PII, inspects outputs, and gates agent tools.

Agentic AI runtime security platform — detects, correlates, and interrupts the Lethal Trifecta attack pattern across all agentic AI systems.

Provenance-aware execution security for DeepSeek Harness. Track sensitive data across tool calls and stop risky side effects before execution.

Local security layer for AI coding agents that blocks secret leaks, destructive commands, unsafe Git operations, and unauthorized network access.

Static security scanner for LLM and agentic source code — finds prompt-injection, context-leak, PII, RAG, and agent-tool vulnerabilities before deployment.

Autonomous Offensive Security, Bug Bounty & Red Teaming Agent Framework powered by Hermes Agent, specialized reasoning skills, and multi-model LLM orchestration.

Six-layer AI agent security for ElizaOS on Solana — prompt injection defense, transaction policies, anomaly detection, and on-chain audit trail. Part of the AgentShield platform (agentshield.pro).

Security and observability for AI agents, for Node. One import gives any TypeScript or JavaScript agent tool-call verdicts and model-run traces in the SecureVector local app. Zero runtime dependencies.

Complete alignment and safety architecture for autonomous AI agents. Membrane security, continuous trust verification, epistemic hygiene, emergence protocols, and defence tools. 30 years of cybernetic research — deployable today.

autonomous red teaming platform; multi-agent offensive-security meta-harness This listing is based on the official GitHub repository, repository metadata, and README-level project context. It focuses on what builders can verify before installing or adapting the project.

Runtime security layer for AI agents — inspect, control, and audit every tool call. Trust-aware data flow, 3-layer intent consistency detection, Merkle audit trail. Drop-in support for LangChain, CrewAI, AutoGen, OpenClaw, MCP, Dify, AutoGPT, n8n.

Flight recorder for AI agents: tamper-evident audit logging, policy enforcement, and incident reports

Autonomous AI agent that finds business-logic vulnerabilities (BOLA/IDOR, price tampering, workflow bypass, data exposure) — Oracle-verified, ~0 false positives

Find every AI agent in your organization and know how risky each one is.

A firewall for AI agents: block prompt injection, guard tool calls, redact PII/secrets.

Practical audit tools and controls for securing MCP servers and agentic AI tool access.