Agent Security
AI Agent Pentesting Tools Compared for Agentic Architectures
Static pentesting tools miss the real threats in AI agents.
Lucia Bennett
Columnist · · 11 min read
Static pentesting tools miss the real threats in AI agents.
Isolated twin environments catch multi-layer attacks that point-in-time testing structurally misses.
Tool definitions stay mutable after approval, turning runtime trust into a security vulnerability.
Attackers hide commands in files agents must read to work.
Real agents with real credentials demand controls that survive when the model's goals get hijacked.
Attackers map AI agents' capabilities, memory, and boundaries before exploiting them.
Agents expose attackers to trust boundaries that form at runtime, not design time.
Attackers exploit agentic systems by embedding malicious instructions in content agents retrieve.