Autonomous-Agents

A human pentester gets tired, breaks for lunch, eventually gives up. An LLM-driven agent does none of that: it loops around the clock, draws no salary, and costs less every year. Against that adversary two mechanisms combine — decoys written for a reader that is a machine, and a maze of virtual machines that turns every hour of attack into pure expenditure. This article details both, and the four limits to state before any deployment.

A chatbot writes sentences; an AI agent acts — it reads your email, runs code, calls APIs, spends money. That shift moves the risk: it is no longer about making the AI say something forbidden, but about making it do something dangerous. This article explains, with detailed and accessible examples, how these attacks actually work, why naive guardrails fail, and what a decision-maker must demand before putting an agent into production.

Classic security tools hunt for dangerous words: ‘hack’, ‘bomb’, ‘urgent’. But you don’t subvert an AI agent with suspicious vocabulary — you subvert it with the ordinary language of the business: a role, a process, a plausible emergency. And when the agent that monitors, the SIEM that correlates and the auditor that checks are themselves AI agents, the attacker no longer has to defeat a system: it corrupts them in a chain. This article explains that recursive-corruption mechanism and what a decision-maker must demand to break it.