Our AI runs adversarial attacks against your agent the way a real attacker would: prompt injection, data exfiltration, tool misuse, scope violations. You get a clear report on where it breaks, a remediation checklist, and an independent certificate you can show your customers.
The Problem
Your tech team configures system prompts and basic filters. But LLMs are probabilistic, non-deterministic engines. Standard barriers are structurally fragile under adversarial attacks.
Prompt injection, tool misuse, agent-to-agent exploits, data exfiltration. None of these show up in unit tests or a traditional pentest. They show up in production, or in front of a customer's security team.
Models update, tools get wired in, prompts get edited. A test that passed Monday can fail Friday. Point-in-time security expires the moment the agent changes.
When you sell or deploy an agent, security reviews want evidence it's safe. Self-attestation no longer clears the bar.
How It Works
Our engine, built on specialized adversarial research, studies your agent and autonomously generates attack chains tuned to its architecture, tools, and data access. We run the attacks, measure where it breaks, and turn the result into proof your customers and security reviewers can verify. You don't run anything, we deliver the result.
You request a test for your agent. A quick setup connects it, then our AI runs adversarial attacks against it the way a real attacker would: prompt injection, data exfiltration, tool misuse, scope violations. We show you how many critical, high, and medium issues we found, at no cost.
You see the severity counts for free. The full report, with what each vulnerability is, where it is, and how to fix it, is unlocked when you want the details. Every finding ships with reproduction steps and is mapped to OWASP Agentic, MITRE ATLAS, and the EU AI Act.
Once your agent passes the safety threshold, we issue the Agensure ADR (Agent Deployment Readiness) Certificate. It features a unique QR code linking to a real-time public verification page, plus an embeddable badge. Your customers, partners, and auditors can verify your status instantly.
One Clear Risk Score
A single, clear number from 1 to 100 that tells you how your agent holds up under attack, and what to fix first.
Our engine attacks your agent across multiple risk domains and measures exactly how many attacks succeed in making it drift, leak data, or break its rules. Every failure is reproducible, so you can verify it yourself.
Part of an agent's risk comes from the underlying model, part from your specific system prompt and setup. We test both together, so the score reflects your real agent, not a generic benchmark.
A support chatbot and an agent wired to transaction APIs carry very different real-world risk, even on the same model. We weight the score by the operational authority your agent actually holds.
We publish the methodology behind the score, so it's clear how it's calculated. The exact attack vectors and their sequence stay proprietary, to keep them from being gamed.
The Report
You see how many issues we found for free. Unlock the full report when you want the details: every vulnerability, where it is, how to reproduce it, and exactly how to fix it.
Vulnerability name and exact location
Reproduction steps and proof of concept
Severity rating (CVSS) and framework tags
Concrete fix instructions and a remediation checklist
Every finding mapped to OWASP Agentic, MITRE ATLAS, NIST AI RMF, and the EU AI Act
What a finding looks like
Reproduction · 4 steps
The Support Agent exposes a refund tool meant for small goodwill credits, capped by policy.
The tester frames the request as a series of small, individually plausible steps.
Escalated context lets the agent compose a payout beyond the cap, with no human review.
The refund executes, because the limit lives in the prompt and not in the tool or the backend.
Remediation
Enforce the cap and rate limit inside the tool and the backend, require human approval above a low threshold, and give the agent its own scoped identity. Agensure re-tests the fix automatically.
Framework mapping
Continuous
Models get updated by their providers. Tools get wired in. Prompts get edited. Every one of these changes can open a vulnerability that wasn't there at your last test. A point-in-time check expires the moment your agent changes.
We re-run the attacks on a regular cadence, so your certificate reflects what your agent does today, not what it did at sign-off. When something breaks, you know before your customers do.
The Process
Your first test is free.
You request a test for your agent. A quick setup connects it to our engine, which runs adversarial attacks against it: prompt injection, data exfiltration, tool misuse, scope violations. You get your initial Agensure Risk Score and the count of issues we found, at no cost.
You unlock the full report: every vulnerability, where it is, reproduction steps, and a concrete remediation checklist. You review the empirical failures and see exactly what to fix, mapped to OWASP Agentic, MITRE ATLAS, and the EU AI Act.
We re-run the attacks on a regular cadence, so daily prompt changes or silent foundation-model updates don't introduce new vulnerabilities without you knowing. A test that passed today can fail tomorrow, and we catch it.
Once your agent passes the safety threshold, we issue your Agensure ADR Certificate: verified proof that your agent has been tested. Valid for 90 days, renewed through continuous monitoring.
In your dashboard
What your customers see
Pricing
The test is free. You always see how many critical, high, and medium issues we found, at no cost.
You only pay to unlock the full report: what each vulnerability is, where it is, and how to fix it.
Pricing scales with your company size and is capped per engagement. No surprise invoice.
The full report includes a remediation checklist and a re-test after you fix.
On pass, your ADR Certificate with a live public QR verification page.
You see where your agent breaks before you pay a cent. Unlock the details only when you want them, at a price that scales with your size and never goes above a fixed cap.
Full pricing available on request.
Privacy Architecture
We test in an isolated environment. You scope exactly what's in bounds, and destructive actions are never executed blind.
The Team
Most AI compliance is built by lawyers who've never seen an agent fail, or engineers who've never read a policy. We've lived both sides.
Experience scaling revenue at SaaS hypergrowth companies in EU enterprise markets, both from 0-1 and from 1-10. Saw enterprises stall AI deals over trust and safety, with no independent way to verify an agent.
LinkedInMS in Cybersecurity from TalTech, thesis on multi-agent frameworks for prompt injection dataset generation. SANS SEC540 and SEC488 trained. Spent years finding better ways to do things by breaking them first. Now builds the system that stress-tests and certifies AI agents for production.
LinkedInWhy Now
Four forces converging at once.
Enterprise and regulated buyers increasingly block AI deployments until an agent's safety is independently verified. Self-attestation no longer clears a security review, and that gate is what stalls deals today.
As agents act autonomously with real customers, a single failure becomes a public, brand-level incident, not a quiet bug. Higher autonomy means higher stakes.
Every software company is shifting toward autonomous agents for customer support, e-commerce, and sales. Autonomous action is the product. Someone has to verify it's safe before a breach occurs.
EU AI Act transparency obligations apply from August 2026, with heavier high-risk frameworks following in 2027–2028. As those deadlines approach, independent testing of AI agents moves from nice-to-have to expected.
Trust
Our engine is built on specialized adversarial research. We know how agents fail because breaking them is what we do.
We collect only what an engagement needs, and access is scoped to the task. Your customers' data and production keys are never in scope.
Every finding we report is reproducible. You can verify it yourself, not take our word for it.
Testing runs in an isolated environment. Engagement data is encrypted and retained only as long as needed to deliver your report.
FAQ
