Penetration testing for AI agents, LLMs, and RAG pipelines
Shipping an AI agent means shipping something that reads untrusted content, holds credentials to real tools, and can be persuaded to act. Agentic AI penetration testing probes what an attacker can make your agents do, across tool use, retrieval, and the systems they can reach.
This page is about testing your AI agents, LLM apps, and RAG systems. Looking for our autonomous agent that runs the pentest for you? See agentic penetration testing and API penetration testing. If you're comparing autonomous testing tools, see how Operator stacks up against XBOW, an autonomous AI pentest agent.
The attack surface of an AI that acts
Actions, not just words
Coercing an agent into calling tools, APIs, or functions outside its intended authority, turning a helpful assistant into a way into your systems.
Content becomes command
Hostile instructions arriving through documents, web pages, tickets, and tool output the agent reads on a user's behalf, then acts on.
Trust boundaries
Agents scoped wider than the user driving them, and confused deputy chains across connected systems that no single tool was meant to allow.
The category conventional test plans miss
An agent is not a chatbot. It has hands. It reads untrusted content, holds credentials, and takes actions, which means an injection is no longer a bad answer, it is an action taken against your systems. Conventional test plans were not written for that.
Operator tests agentic AI the way an adversarial user would, mapped to the OWASP Top 10 for LLM Applications and extended to the tools, retrieval, and APIs your agents reach.
- Tool and function call abuse across every capability the agent holds.
- Indirect prompt injection through every channel the agent ingests.
- RAG and retrieval poisoning, and cross tenant reads in the vector store.
- Data exfiltration through actions and rendered output.
How we test what your agents can be made to do
Testing an agent is not a scan. It is an adversarial exercise against a system that reads untrusted content, holds real credentials, and takes actions on someone's behalf. We run it with the same proof-based method behind our agentic pentesting: reproduce the abuse, chain it as far as it goes, and report only what we can demonstrate.
Map the agent's world
We inventory what the agent can reach: the tools and functions it can call, the APIs and data stores behind them, the content channels it ingests, and the identity and scope it runs under. That map is the real attack surface, not the chat box in front of it.
Inject through real channels
We plant hostile instructions where the agent actually reads them, documents, web pages, tickets, email, tool output, and retrieved context, not only by typing at the prompt. Indirect injection is where production agents break.
Abuse tools and cross boundaries
Where an injection lands, we push it into action: calling tools outside intended authority, escalating scope, and walking confused-deputy chains across the connected systems the agent can touch. The question is what an attacker can make it do, not just say.
Prove impact and report
Every finding is carried through to real effect, reproduced, and rated with a CVSS v3.1 vector, with the requests, payloads, and agent traces attached. Anything we cannot demonstrate does not reach your report.
What agentic AI penetration testing covers
Model-only testing stops at what the model says. Agentic testing follows the system around it, mapped to the OWASP Top 10 for LLM Applications and extended to the tools, retrieval, memory, and orchestration your agents depend on.
Every channel, not just the prompt
Direct and indirect injection through documents, web content, tickets, email, and tool output the agent reads on a user's behalf, including payloads that survive summarization and retrieval.
Actions beyond authority
Coercing the agent into calling tools, functions, and internal APIs outside its intended scope, and turning a single capability into a foothold in the systems behind it.
The retrieval layer as attack surface
Poisoned documents in the vector store, cross-tenant reads, and retrieved context that quietly becomes instruction the agent acts on later.
Scope and trust boundaries
Agents scoped wider than the user driving them, permissions that should never combine, and confused-deputy chains no single tool was meant to allow.
Persistence that carries the attack
Poisoning conversation memory, long-term stores, and cached context so a manipulation planted once keeps steering the agent across later sessions.
Orchestration and connectors
Abuse that hops between cooperating agents and connected services, including MCP server security testing, where one agent's output becomes another's trusted input.
Where this fits alongside LLM security testing
Model-level LLM security testing asks what the model can be made to say: jailbreaks, unsafe output, and data leaked through the response. That work still matters, and we do it. But an agent adds a second, higher-stakes question, what can it be made to do once a manipulated response turns into a tool call, an API request, or a write to a real system.
Agentic AI penetration testing owns that second question. It is a specialized cut of agentic pentesting aimed at systems that act, and it pairs naturally with continuous testing of the rest of your attack surface rather than living in a silo.
- Model layer, jailbreaks, unsafe generation, and sensitive output, mapped to the OWASP LLM Top 10.
- Retrieval layer, RAG poisoning, cross-tenant reads, and context that becomes instruction.
- Action layer, tool and API abuse, excessive agency, and confused-deputy chains.
- Proof, every finding reproduced and rated, with agent traces you can verify.
Common questions
What is agentic AI penetration testing?
Security testing of AI systems that take actions: agents that call tools, browse, run code, or reach internal APIs, along with the RAG pipelines and model context they depend on. It probes what an attacker can make the agent do, not just what the model can be made to say.
How is it different from LLM security testing?
LLM testing focuses on the model and its prompts. Agentic AI testing focuses on the system around it: tool and function call abuse, excessive agency, data exfiltration through actions, and untrusted content that becomes instructions the agent acts on.
What does it test?
Prompt injection through every channel the agent reads, tool and API abuse, privilege and trust boundary violations, RAG and retrieval poisoning, and the confused deputy chains that turn a helpful agent into an attacker's proxy.
How do you test an agent without breaking production?
Scope is a hard boundary set before the run and enforced in software, and destructive actions are gated. We prefer a staging or sandboxed instance for the first pass, then agree exactly which tools and data the agent may touch in production. The goal is to prove impact safely, not to cause it.
Do you test RAG pipelines and vector stores?
Yes. Retrieval is a primary attack surface: we test for poisoned documents that become instructions on retrieval, cross-tenant reads in the vector store, and context that carries an injection forward into a later action the agent takes.
What frameworks do you map findings to?
The OWASP Top 10 for LLM Applications as the baseline, extended to the agent-specific surface it does not fully cover: tool and function abuse, excessive agency, memory poisoning, and multi-agent chains. Every finding is also rated with a CVSS v3.1 vector.
Can you test multi-agent systems and MCP tools?
Yes. We test how abuse hops between cooperating agents and connected services, including MCP-style tool servers, where one agent's output becomes another agent's trusted input and a single injection can travel further than any one component was designed to allow.
Test what your agents can be made to do
Describe the agent, the tools it can reach, and the content it reads, and we will show you what an attacker can make it do.