Purpose-Built for AI Agent Attack Surfaces

Every enterprise shipping AI features is deploying AI agents. Most have never had them security-tested.

The attack surface is unlike anything traditional penetration testing was designed to assess. An AI agent doesn’t just run code — it reads instructions, calls tools, maintains memory, and takes autonomous actions. Each of those capabilities is an attack vector.

What Adversaries Are Actually Doing

Prompt injection embeds adversarial instructions in data your agent reads — a document, an email, a web page. The agent executes those instructions as if they came from you. Your agent becomes the adversary’s proxy inside your own systems.

Tool poisoning compromises one of the tools your agent calls. The agent, acting in good faith, retrieves and executes attacker-controlled data. If your agent calls a retrieval tool to fetch customer records, a poisoned tool returns data that hijacks the agent’s next action.

Memory manipulation injects false context into your agent’s memory store. Future agent sessions execute based on corrupted context. An adversary can persist across agent restarts without maintaining network access.

Agentic privilege escalation uses your agent’s tool access — write access to your CRM, your email, your database, your payment rails — to move laterally into systems the agent can reach but the adversary cannot.

The APEX Framework

pentest.qa’s APEX methodology (Agentic Penetration Exercise) is a documented framework for systematically testing these attack vectors. Human senior researchers drive all five phases. AI agents automate enumeration, fuzzing, and injection sweeps in parallel — covering attack surface that would take a human team weeks to enumerate manually.

The result: faster, deeper, and more comprehensive coverage than any manual-only approach — without the false-positive noise that purely automated tools produce.

Why Engineering Teams Are Responsible for AI Security

The security of an AI agent is not a CISO problem alone — it is an engineering problem. The attack surface is created at the code level: in the system prompt, in the tool definitions, in the output handling pipeline, in the agent memory architecture. A CISO cannot write a safer system prompt. Your QA team can.

Traditionally, security and QA were separate disciplines. Security assessed what QA had built, after the fact. AI changes this boundary permanently. The prompt injection surface is a direct function of how your engineering team writes system prompts and processes user input. The tool poisoning surface is a direct function of how your agent validates tool outputs. The excessive agency surface is a direct function of what permissions your team granted the agent at provisioning time.

Engineering teams that understand their AI attack surface build more secure agents from the start. The APEX engagement includes a technical workshop with your engineering team — not just a findings report for your CISO — because the remediation happens in code.

Furthermore, CI/CD pipelines now deploy AI agents continuously. A new system prompt, a new tool integration, a new model version — each represents a change to the attack surface. An annual red team exercise is a snapshot of a system that may look very different twelve months later. Enterprises with mature AI security programs combine the APEX exercise with continuous Security QA Integration — automated checks in the pipeline between annual engagements.

Compliance frameworks are moving in the same direction. ISO 27001 Annex A controls, SOC 2 CC6 and CC7 requirements, and the NIST AI Risk Management Framework all require documented evidence that security testing has been conducted against AI-specific attack vectors. An Agentic Red Team Exercise produces that evidence — and produces it for a system your auditors have never assessed before.