Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.
Long-running AI agents need security controls that account for drift over hours or days, not just per-request checks, because permissions, context, and risk all shift over an extended task.
Agent loops and runaway actions turn a single reasoning mistake into repeated tool calls at machine speed, which makes cost and resource controls a security concern, not just a budgeting one.
Safe retry and recovery for AI agents means designing failure handling so that a retried or resumed action can't accidentally duplicate, escalate, or bypass the checks the original attempt was supposed to go through.
Human approval in agentic workflows works only when it's placed at genuinely consequential decision points and designed to resist becoming a rubber stamp over time.
Limiting an AI agent's blast radius means designing so that any single bad decision, however it happened, can only cause bounded, recoverable damage.
Preventing unauthorized agent actions means enforcing authorization at every tool call, not trusting the agent's own judgment about whether an action is appropriate.
Context manipulation attacks shape what an AI agent believes is true about its current task by controlling the surrounding information it reads, without ever issuing a direct command.
Memory poisoning happens when an attacker gets a false or malicious fact stored in an agent's persistent memory, so it gets recalled and acted on in future sessions long after the original interaction ended.
Goal manipulation attacks work by subtly redefining what an AI agent believes it's trying to accomplish, so the agent's own reasoning carries out the attacker's objective without ever appearing malfunctioning.
AI agents get hijacked when an attacker plants instructions the agent treats as legitimate, redirecting its plan toward the attacker's goal instead of the user's.
Agent-to-agent trust needs the same verification discipline as any external input, because agents that accept each other's output without checking it turn one manipulated agent into a system-wide problem.
Multi-agent systems need explicit security boundaries between agents, because treating internal agent-to-agent messages as automatically trustworthy is one of the fastest ways a single compromise spreads.
