AI Agent Security is a security assessment of AI agents that use LLMs, tool calling, RAG, memory, and access to systems. We test whether such an agent can be exploited to cause data leakage, abuse available tools, bypass access controls, or pivot into adjacent infrastructure.

Modern AI agents are no longer just an interface to a language model. They interact with tools, APIs, knowledge bases, files, corporate services, internal systems, and user data.

As a result, an AI agent expands a company’s attack surface and requires a dedicated security testing approach.

AI Agent Security assesses whether an AI agent or agentic system can be manipulated to act against its intended purpose. We simulate realistic attack scenarios in which the agent may be induced to perform unintended actions, bypass access controls, disclose sensitive data, misuse tool calling, or abuse unsafe integrations.

What We Test

Agent Tools and Actions

We analyze which tools, functions, and external actions are available to the AI agent and test whether they can be abused in unsafe or unintended ways.

In particular, we test whether the agent can be induced to:

perform unintended actions;
pass dangerous or unvalidated parameters;
bypass restrictions or policy controls;
combine multiple permitted actions into a harmful execution chain.

Access Сontrol and Authorization

We evaluate whether a user can gain more access through the AI agent than their role is supposed to allow.

This includes testing for risks such as:

access to other users’ or restricted data;
execution of hidden or unexpected actions;
bypass of access controls;
privilege escalation.

Knowledge Bases, RAG, and Agent Memory

We analyze how the agent retrieves information, what data enters its context, and how RAG, vector stores, shared memory, or other long-term memory mechanisms operate.

We test for risks including:

leakage of commercially sensitive information;
disclosure of personal data;
indirect prompt injection through documents, web content, or other external sources;
poisoning of memory, knowledge bases, or vector stores;
data leakage across users or sessions.

Backend and Integrations

AI agents often operate on top of APIs, databases, internal services, third-party platforms, and external integrations. Therefore, we test not only the agent’s behavior, but also the technical components it interacts with.

Our testing may include:

SQL injection;
SSRF;
IDOR;
authentication and authorization flaws;
unsafe parameter handling;
the possibility of pivoting from a compromised agent into other parts of the infrastructure.

Risks We Help Identify

We help uncover:

direct prompt injection indirect prompt injection unsafe tool usage insecure agent chaining broken access control privilege escalation data exfiltration cross-user or cross-session data exposure memory poisoning PII leakage leakage of commercially sensitive information unsafe integrations backend and API vulnerabilities risks of pivoting into internal infrastructure

Who This Service Is For

This service is relevant for organizations that:

implement AI agents in internal processes;
build SaaS products with AI assistants or agentic workflows;
grant agents access to CRM, ERP, email, knowledge bases, internal APIs, and similar systems;
use RAG, shared memory, vector stores, or multi-user agentic scenarios;
want to assess real-world risk not only at the model level, but across the entire agentic system.

What You Receive After Testing

A practical report that shows not only the identified vulnerabilities, but also realistic scenarios for how they could affect your AI agent, data, or integrations.

The report includes:

a description of identified risks and attack scenarios;
technical details and reproduction steps;
an assessment of impact on data, users, business logic, and infrastructure;
a severity rating for each finding;
examples of unsafe or unintended agent behavior;
remediation recommendations;
prioritized fixes;
retesting after remediation is implemented.

AI Agent Security Assessment