--- title: Validate Your Own Agents Before Shipping description: Test your AI agent before shipping and validate it in CI. ZioSec runs AI-driven adversarial attacks against your own agents, with audit-ready evidence for reviews. url: https://ziosec.com/use-cases/validate-your-agents --- # Validate Your Own Agents Test your AI agent before you ship it, and before a customer puts it through a security review. ZioSec turns AI loose on your agents, generating bespoke attack trees against your exact architecture, tools, and data access, then hands back audit-ready evidence you can drop into a release gate or a buyer's questionnaire. [Book a Demo](/demo) | [See a Sample Report](/sample-report) ## Find the failures before your customers do Prompt injection, tool misuse, memory poisoning, and agent-to-agent exploits do not surface in unit tests or a traditional pentest. They surface in production, or in front of an enterprise buyer's security team. ZioSec validates your own agents the way a real adversary would, so the first deep attack against your agent is one you ran on purpose. ## AI attacks AI, tuned to your agent Static scanners replay a fixed list of known prompts. ZioSec does the opposite. Our AI studies your agent's architecture, its connected tools, and what data it can reach, then autonomously generates bespoke, deep-chained attack trees unique to that agent and executes them in real time. When your agent exposes a new tool or a new data source, the attacks adapt instead of going stale. This is the core capability, and it is the part a checklist cannot copy. ## A validation gate for every release Run a full adversarial campaign before a launch, then re-run it on every meaningful change. Each run reports which attacks were attempted, which succeeded, the severity, and exact reproduction steps, so engineering can fix the real path instead of guessing. Catch the prompt injection that pivots into an unsafe tool call while it is still a finding in a report, not an incident in production. ## Validate agents in your pipeline, not after it Wire ZioSec into your build through the API and treat agent security like any other test that can fail a pull request. Set thresholds (for example, block on any high-severity tool-misuse finding), get results back as structured output, and keep a run history per branch. Validation in CI means a regression in your agent's safety posture shows up in the same place as a broken test, not three weeks later in a buyer's questionnaire. ## Show up to the review with evidence in hand When an enterprise prospect sends a security questionnaire about your AI agent, you can answer with proof instead of promises. ZioSec produces audit-ready evidence: a packet listing the attacks attempted, which succeeded, severity ratings, reproduction steps, control-level mappings to OWASP AISVS, MITRE ATLAS, ISO 42001, NIST AI RMF, EU AI Act, and AIUC-1, plus timestamps, exportable for your GRC and trust workflows. That packet often is the difference between a stalled deal and a signed one. ## Live attacks, safe by design These are real attacks against your real agents, so you stay in control of the blast radius. You scope exactly what is in bounds: which agents, which tools, which data classes. Destructive actions are simulated or approval-gated, never executed blind. There is a one-click stop, a full audit log of everything attempted, and rate limits to protect production. It is an offensive capability you operate, not a black box you hope behaves. ## Every finding lands in a recognized standard Behind the attacks is our methodology ([A2OSF](/methodology)), the high-resolution taxonomy we classify each finding in before exporting to the six public standards security and compliance teams already trust: OWASP AISVS, MITRE ATLAS, ISO 42001, NIST AI RMF, EU AI Act, and AIUC-1. So a single validation run serves two audiences at once: your engineers get reproduction steps, and your reviewers and auditors get control-level evidence in a vocabulary they recognize. ## Three ways to start validating Run it yourself, wire it into your build, or have us run a scoped engagement first. The low-commitment on-ramp credits back in full. - **Self-serve on the platform.** Scope your agents, launch adversarial campaigns on demand, and re-run before every release. Findings come back mapped to all six frameworks with reproduction steps. - **Automated in CI.** Call the API from your pipeline, set pass/fail thresholds, and fail the build on regressions in your agent's safety posture. Works with custom agents, Claude Code, and any agent built on MCP or A2A protocols. - **A scoped engagement first.** Not ready for continuous? Start with a $10,000 scoped pentest engagement as a one-time validation. 100% of the fee credits toward an annual platform subscription. ## Frequently asked questions **How do I test my AI agent before shipping?** Point ZioSec at the agent and scope what is in bounds: which tools, which data classes, which agents. ZioSec then generates attack trees specific to that agent and runs them, returning attacks attempted, what succeeded, severity, and reproduction steps. Run it as a pre-release gate, then re-run it on every meaningful change. **Can I validate my agent in CI?** Yes. Use the API to run a validation campaign from your pipeline, set pass/fail thresholds (for example, block on any high-severity finding), and get structured results back per branch. A regression in your agent's safety posture fails the build in the same place a broken test would. **What makes this different from a prompt-injection scanner?** A scanner replays a fixed list of known prompts. ZioSec uses AI to attack your agent, autonomously generating bespoke, deep-chained attack trees tuned to your specific architecture, tools, and data access, executed in real time. When your agent changes, the attacks adapt instead of going stale. **Is it safe to run live attacks against my own agents?** Yes, it is safe by design. You scope the blast radius: which agents, tools, and data classes are in bounds. Destructive actions are simulated or approval-gated, never executed blind. There is a one-click stop, a full audit log, and rate limits to protect production. You operate the capability and stay in control throughout. **Which agents and frameworks does this support?** ZioSec validates custom agents, Claude Code, and any agent built on MCP or A2A protocols. Every finding is mapped through our methodology (A2OSF) into OWASP AISVS, MITRE ATLAS, ISO 42001, NIST AI RMF, EU AI Act, and AIUC-1. **What do I hand to a customer's security review?** Audit-ready evidence: a packet listing the attacks attempted, which succeeded, severity ratings, reproduction steps, control-level framework mappings, and timestamps, exportable for GRC and trust workflows. It answers a security questionnaire with proof instead of promises. ## Related - [For Developers](/developers) - [Agentic Software on Demand](/use-cases/agentic-software-on-demand) - [AI Agent Pentesting (Scoped Engagement)](/ai-agent-pentesting) ## Ship the agent. Pass the review. Keep the proof. Book a demo to validate your own agents against AI-generated attack trees, or see a sample report to look at the exact evidence packet your engineers and your reviewers will get. [Book a Demo](/demo) | [See a Sample Report](/sample-report) ## Contact Email [info@ziosec.com](mailto:info@ziosec.com), call +1-720-807-2737, or book a demo at [/demo](/demo). ZioSec is based in Boulder, Colorado.