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 happens after you submit specs
1. Context
We inspect the system, constraints, and where delivery or architecture risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory track, scoped build, or a clear signal that the work is not ready yet.
3. Next Step
If there is a fit, we define the shortest path to a useful engagement and a production-ready outcome.
Before your users break your agent, we do.
This is NOT a security pentest. NOT a compliance certification. It is adversarial functional testing — the same class as chaos engineering or load testing, applied to AI agents.
"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’s supposed to do. Adversarial testing tests whether the agent can be made to do what it is NOT supposed to do. 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 — customer service, internal ops, financial processing, document interpretation, legal research. Not chatbots on marketing pages.
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: what was tested, what was not
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.
Not a Fit
- 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 |
|---|---|
| Tier 1 — Adversarial Assessment: $3,000-$6,000 | 5 business days. One production agent or pipeline. Written report + findings call. |
| Tier 2 — Remediation Sprint: $8,000-$20,000 | Requires assessment first. Implements guardrails, cognitive firewalls, escalation path fixes, tool call validation, output validation gates. Includes regression test suite. |
| Tier 3 — Ongoing Adversarial Retainer: $4,000-$8,000/month | For organizations deploying agents continuously. Monthly assessment pass on new versions. Monthly report. |
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
Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.
1. Context
We review the system, constraints, and where risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory, sprint, or pause.
3. Next Step
If there is a fit, we define the shortest useful engagement.
No SDRs. A Principal Engineer reviews every submission.