sage

Sage Safety for Agents — Agent Detection & Response for AI coding assistants Sage is a lightweight security layer that protects AI agents from executing dangerous actions. It intercepts tool calls — shell commands, URL fetches, file writes — and checks them against multiple threat detection layer...

sigma-ai

AgentShield Sigma Rules What is This Repository? This repository contains detection rules that help identify when an AI agent is being attacked or manipulated. Think of it as a library of "threat signatures" -- each rule describes a pattern that, when matched against an agent's log data, signals...

wardgate

Wardgate - AI Agent Security Gateway Wardgate is a security gateway that sits between AI agents and the outside world -- isolating credentials for API calls, isolating SSH keys for remote command execution, and gating command execution in remote environments conclaves. Give your AI agents access ...

hush

Portable, open specification for AI agent security rules Spec · Docs · Rulesets · JSON Schema HushSpec is an open policy format for AI agent security rules. It defines what an agent may do at runtime, including filesystem access, network egress, tool usage, secret detection, and more, without...

MakerChecker

🛡️ MakerChecker The open-source security layer for AI agents. Deny-by-default enforcement, human approvals, and a cryptographically signed audit trail — so your agent runs only what it's granted and provably can't approve its own work. Website · Live Demo · mc scan · Docs Your agents keep running...

Agent-Security-Regression-Harness

Arnés de Regresión de Seguridad de Agentes OWASP El Arnés de Regresión de Seguridad de Agentes OWASP es un arnés de pruebas de código abierto y neutral respecto al proveedor para ejecutar escenarios de regresión de seguridad ejecutables contra aplicaciones agénticas y sistemas integrados con MCP...

secure-agent-playbook

Secure Agent Playbook An open-source security playbook for AI agents. Structured, OWASP-grounded procedures that enable agents to perform security engineering tasks — from code review to AI agent security audits. Table of Contents Why Use This? What This Is Quick Start Skills Catalog Agents Examp...

tealtiger

TealTiger AI Agent Security & Governance SDK Deterministic governance, guardrails, cost tracking, and policy management for LLM applications. Open source. TypeScript + Python. Works with any provider...

node9-proxy

🛡️ Node9 What did your AI agent actually do? Find out. Node9 sits between your AI agent and the tools it can use — what it's already been doing, against risky actions in real time, and what happened over any time window...

aegisagent

AegisAgent — AI Agent Runtime Security Gateway Puerta de enlace de seguridad en tiempo de ejecución para AI Agent: vincula criptográficamente cada llamada de herramienta a la finalización real del modelo, con ejecución de políticas de confianza cero, aislamiento en sandbox y aprobación humana...

CVE-2026-40145

A vulnerability exists in the interaction between a Endpoint Privilege Management Windows Deployment support utility and the agent's tamper protection controls. Under certain conditions, the protections applied to the utility process may not be enforced as intended...

CVE-2026-57282

Jenkins Git client Plugin 6.6.0 and earlier does not correctly escape the workspace directory name when it is embedded into a generated SSH wrapper script, allowing attackers able to control the name of a build's working directory to execute arbitrary operating system commands on the agent...

-cascade-scan

cascade-scan AI Agent security evaluation framework — autom...

AI Agent Security: Automating Workflow Without Creating Prompt Injection or Data Leak Risks

AI agent security starts with a simple fact: the more authority an agent has, the tighter its access…...

Agent Security Is a Systems Problem

We take the position that agent security must be approached as a systems problem: the AI model powering the agent must be treated as an untrusted component, and security invariants must be enforced at the system level. Through this lens, efforts to increase model robustness the dominant viewpoint...

Ghost in the Agent: Redefining Information Flow Tracking for LLM Agents

Autonomous Large Language Model LLM agents are increasingly deployed to conduct complex tasks by interacting with external tools, APIs, and memory stores. However, processing untrusted external data exposes these agents to severe security threats, such as indirect prompt injection and unauthorize...

Architecture Matters for Multi-Agent Security

Multi-agent systems MAS, composed of networks of two or more autonomous AI agents, have become increasingly popular in production deployments, yet introduce security risks that do not arise in single-agent settings. Even if individual agents exhibit robust security, architectural decisions...

Towards Modeling Cybersecurity Behavior of Humans in Organizations

We undertake a comprehensive and structured synthesis of the drivers of human behavior in cybersecurity, focusing specifically on people within organizations i.e., especially employees in companies, and integrate key concepts such as awareness, security culture, and usability into a coherent...

Targeted Bit-Flip Attacks on LLM-Based Agents

Targeted bit-flip attacks BFAs exploit hardware faults to manipulate model parameters, posing a significant security threat. While prior work targets single-step inference models e.g., image classifiers, LLM-based agents with multi-stage pipelines and external tools present new attack surfaces,...

Extending the Formalism and Theoretical Foundations of Cryptography to AI

Recent progress in Large Language Models LMs has enabled the development of autonomous LM-based agents capable of executing complex tasks with minimal supervision. These agents have started to be integrated into systems with significant autonomy and authority. The security community has been...