Agentic AI Security
An autonomous agent isn't an API: it decides, chains tools, and remembers. Each of those capabilities is a distinct attack surface.
An AI agent can execute exactly the code it has and still be hijacked. A hidden instruction in a document can redirect its goal, or a tool can do far more than its name suggests. The model obeys without necessarily understanding the full context.
We test the agent as a complete system, not the prompt in isolation: goal hijacking through indirect injection, tool abuse and excessive permissions, supply chain risks from plugins and third-party providers, and memory poisoning and persistence across sessions.
When these agents process confidential data, we run them in confidential compute environments so neither the provider nor we can access the information in the clear. More in Confidential Compute (TEE).
Spec sheet
- Includes
- Goal hijacking and indirect injection testing, tool use and permissions audit, supply chain analysis of plugins and third-party providers, memory poisoning and persistence testing, and risk evaluation in environments with sensitive data.
- Deliverable
- A map of the agent's attack surface, reproducible abuse scenarios with clear impact, and concrete controls: tool limits and validation, memory and context isolation, permission restrictions, and untrusted input validation.
- Who it's for
- Teams deploying autonomous agents with access to tools, data, or high-impact actions: internal copilots, operations agents, assistants with write permissions, and similar.
Want to review this front with us?
A technical conversation, no sales script. We'll tell you honestly whether the risk is in your code, your incentives, or your agent, and whether we can help.