AI Agent Security Review
Structured adversarial testing of production AI agents. We find failure modes — prompt injection, goal hijacking, tool misuse, state confusion — before they become incidents.
What you get back
- 1. Diagnosis What works, what is blocked, and why.
- 2. Recommendation Audit, advisory, sprint, or pause.
- 3. Scope Next action, boundaries, and timing.
Before your users break your agent, we do.
This is adversarial functional testing, the same class as chaos engineering or load testing, applied to AI agents. Security pentests and compliance certifications require a different scope.
"This service provides adversarial functional testing of AI agents. It does not constitute a security penetration test, security audit, or compliance certification. It does not attest to compliance with NIST AI RMF, EU AI Act, HIPAA, SOC 2, or any other regulatory framework."
The problem
Standard QA tests whether the agent does what it is supposed to do. Adversarial testing tests whether crafted inputs can push the agent outside its allowed behavior. These are different problems. Most production agents have only been tested the first way.
Who this is for: CTO or Head of AI deploying agents in consequential workflows, including customer service, internal ops, financial processing, document interpretation, and legal research.
What We Test
| Attack surface | What We Test |
|---|---|
| Prompt injection | Can a user or input source override the agent’s instructions? |
| Goal hijacking | Can the agent be redirected to pursue a different goal through crafted input? |
| State confusion | Does the agent maintain correct state under adversarial sequences? |
| Tool misuse | Can the agent be induced to call tools in unintended ways? |
| Output manipulation | Can responses be manipulated to produce harmful, incorrect, or off-policy content? |
| Hallucination under adversarial input | Does the agent hallucinate more under adversarial prompts than baseline? |
| Escalation path gaps | If the agent detects uncertainty, does it escalate correctly? Or does it forge ahead? |
What you leave with
Written adversarial assessment report:
- Executive summary: overall risk posture, top 3 findings
- Findings table: attack vector, severity, reproduction steps, recommended fix
- Recommended remediation priority order
- Explicit scope boundary: tested surfaces and excluded surfaces
AW's adversarial testing methodology comes from the Axion Engine — a production multi-model adversarial verification system used in our own R&D pipeline. We apply the same methodology to your production agents.
Best Fit
- CTO or Head of AI deploying agents in consequential workflows
- Board or regulatory question: “Have you tested your agent?”
- Upcoming launch of an agent in a high-stakes workflow
- Post-incident review after an agent produced a bad output
The review covers AI agent security testing, AI agent adversarial testing, prompt injection testing, tool misuse, and state confusion.
Better Routed Elsewhere
- The request is a security penetration test
- The request is a security audit or compliance certification
- The agent is a marketing-page chatbot with no consequential workflow or tool-use risk
How We Engage
| Engagement | What You Get |
|---|---|
| Adversarial Assessment | Scoped review of one production agent or pipeline. Written report and findings call. |
| Remediation Sprint | Requires assessment first. Implements guardrails, cognitive firewalls, escalation path fixes, tool call validation, output validation gates, and regression tests. |
| Ongoing Adversarial Review | Recurring assessment for organizations deploying agents continuously, with findings reports as the system changes. |
Related
Also see: Production AI Audit — if the agent failure is part of a broader system problem.
Deployments in this area
Axion Engine: Adversarial R&D Operating System
Domain-agnostic R&D pipeline where three models attack each other's output across CS, clinical medicine, and IoT firmware.
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Discuss your AI Agent Security Review path
Send the system context, constraints, and pressure. A Principal Engineer reviews it and recommends the next step.
No SDRs. A Principal Engineer reviews every submission.