AI Security
Agentic AI Penetration Testing
Red-teaming for autonomous AI agents and LLM-powered applications — prompt injection, tool and function-call abuse, RAG poisoning, and excessive agency, aligned to the OWASP Top 10 for LLM Applications.
Overview
Agentic AI changes the threat model. When an LLM can call tools, browse, execute code, and act on your systems, a single crafted input can turn into a real action — exfiltrating data, abusing an integration, or escalating privilege. Traditional pentests do not cover these paths.
We red-team your agents end to end: the model, its prompts and guardrails, the tools and APIs it can invoke, its memory and retrieval (RAG) sources, and the orchestration between agents. Findings are mapped to the OWASP Top 10 for LLM Applications and delivered with concrete, engineering-ready mitigations.
Typical engagement: 2–8 weeks, depending on scope, access, and reporting requirements.
What we test
Direct & Indirect Prompt Injection
Jailbreaks and instructions hidden in documents, web pages, emails, or tool output that hijack the agent mid-task.
Tool & Function-Call Abuse
Coercing the agent into unsafe tool calls, parameter tampering, and chaining tools to reach unintended actions.
Excessive Agency
Over-broad permissions, missing human-in-the-loop, and autonomy that lets the agent act beyond its mandate.
RAG & Memory Poisoning
Tainting vector stores, knowledge bases, and long-term memory to steer future decisions and leak data.
Sensitive Information Disclosure
System-prompt and secret extraction, training/context data leakage, and cross-tenant context bleed.
MCP & Multi-Agent Risks
Model Context Protocol server trust, untrusted tool servers, and confused-deputy attacks across agent handoffs.
How the engagement runs
- 1
Architecture Review
Map the agent: models, prompts, tools, data sources, memory, and trust boundaries.
- 2
Threat Modeling
Identify abuse cases unique to agency — actions, integrations, and blast radius.
- 3
Adversarial Testing
Manual and automated red-teaming of injection, tool abuse, and data-poisoning paths.
- 4
Report & Hardening
Mapped findings plus guardrail, least-privilege, and validation guidance — with a retest.
Common questions
- What is agentic AI penetration testing?
- It is security testing focused on AI systems that can take actions — autonomous agents and LLM apps that call tools, browse, run code, or modify data. Beyond testing the model, we test the tools it can invoke, its data and memory, and the orchestration between agents, because that is where real-world impact happens.
- How is this different from normal application testing?
- Agents blur the line between data and instructions: untrusted content can become a command (indirect prompt injection). We test for model-layer attacks, unsafe tool execution, excessive agency, and RAG/memory poisoning — threat classes that traditional web and API pentests do not cover.
- Which frameworks and standards do you use?
- We align to the OWASP Top 10 for LLM Applications, the OWASP Agentic Security initiative, MITRE ATLAS, and NIST AI RMF, combined with classic application and API security testing for the surrounding stack.
- Do you test RAG pipelines and tool/MCP integrations?
- Yes. We assess retrieval pipelines and vector stores for poisoning and data leakage, and we test tool and Model Context Protocol (MCP) integrations for trust, authorization, and confused-deputy issues.
- Can you test without harming production data or models?
- Yes. We prefer staging or sandboxed agents with representative tools and data, agree rules of engagement up front, and use controlled, reversible techniques so testing never damages live systems or data.
Find the gaps before attackers do.
Get a tailored scope and fixed quote for agentic ai penetration testing on a 15-minute call.