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.
Coercing an agent into calling tools, APIs, or functions outside its intended authority, turning a helpful assistant into a way into your systems.
Hostile instructions arriving through documents, web pages, tickets, and tool output the agent reads on a user's behalf, then acts on.
Agents scoped wider than the user driving them, and confused deputy chains across connected systems that no single tool was meant to allow.
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.
Planck 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.
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.
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.
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.
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.
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.
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.
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.
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.
Poisoned documents in the vector store, cross-tenant reads, and retrieved context that quietly becomes instruction the agent acts on later.
Agents scoped wider than the user driving them, permissions that should never combine, and confused-deputy chains no single tool was meant to allow.
Poisoning conversation memory, long-term stores, and cached context so a manipulation planted once keeps steering the agent across later sessions.
Abuse that hops between cooperating agents and connected services, including MCP-style tool servers, where one agent's output becomes another's trusted input.
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.
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.
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.
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.
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.
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.
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.
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.
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.