The article explains how memory poisoning attacks persist across sessions in AI agents, details the memoryfield format for auditable storage, proposes a secure write path with trust levels and source tracking, and provides detection queries for identifying poisoned memories.
This article explains how memory poisoning attacks persist across AI agent sessions, details the MINJA attack technique, and proposes auditable memory storage formats, secure write paths with trust scoring, and detection methods including integrity checks and activation monitoring to defend against persistent memory corruption.
The article explains how production‑grade AI agents require a full‑stack security framework—covering input sanitization, runtime policy enforcement, output verification, and audit—to mitigate ten OWASP attack surfaces such as prompt injection, tool misuse, memory poisoning, and cascading failures, with practical defense layers and red‑team testing guidance.
AgentWard is a full‑stack security operating system for autonomous AI agents that protects the entire lifecycle—from startup and input handling to memory, decision alignment, and execution—using layered defenses that have already blocked over 95% of simulated attacks in real‑world tests.
