Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.
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.
Threat modeling an AI agent means mapping its plans, tools, and data sources step by step, then ranking each possible action by impact and reversibility rather than by sensitivity alone.
An AI agent's attack surface spans its inputs, memory, tool integrations, and inter-agent communication, each offering a different way to redirect what the agent does next.
Autonomous actions change the AI threat model by making tool calls, not just generated text, the thing an attacker is really trying to control.
AI assistants respond with information while AI agents take action, and that action capability is what changes the security requirements from output review to permission control.
AI agents create new security risks because they combine language-driven decision making with the ability to act, turning ordinary manipulation attempts into real-world consequences.
