AI agents are everywhere. They're booking meetings, writing code, managing customer support queues, and making decisions that used to require a human in the loop. But the security conversation hasn't caught up.
The Problem
Most organizations treat AI agents like any other software component - they test the API, check the auth, maybe fuzz some inputs. But AI agents have an entirely different threat model.
The attack surface isn't just the API endpoint. It's the prompt, the context window, the tool-calling interface, and every piece of data the agent ingests during its workflow.
Where Traditional Testing Falls Short
A conventional penetration test won't find prompt injection vulnerabilities. It won't catch an agent that can be socially engineered into leaking system prompts. It won't identify the subtle ways an agent's behavior changes when adversarial content is embedded in its retrieval context.
These are the gaps we focus on.
What Attackers Actually Do
- Indirect prompt injection - embedding malicious instructions in documents, emails, or web content the agent processes
- Tool manipulation - tricking agents into calling tools with attacker-controlled parameters
- Context poisoning - corrupting RAG retrieval to shift agent behavior over time
- Guardrail evasion - systematically probing safety filters to find bypass patterns
The Bottom Line
If you're deploying AI agents in production, you need adversarial testing that understands how these systems actually fail. Not how software fails. How intelligence fails.
That's what we do at Hijack Security.