We don’t do green lights or paper reports.
We show what’s actually exploitable
and how to fix it.
For AI applications, we combine manual verification, source code analysis (when available), and runs of autonomous pentest agents such as CAI to increase coverage.
Industries We Protect
As AI becomes part of critical systems, our pentesting is built for high-risk industries, enterprise SaaS, and teams launching AI copilots, agents, and RAG features who need security they can rely on
What We Test
We test the parts of your AI stack that break in the real world.
Our Approach
We build trust in your technology. The goal is simple: reduce unknown vulnerabilities, protect valuable data, and keep your product reliable and safe.
Methodologies
We don’t just name-drop frameworks — we apply them in every AI pentest. Our work is guided by proven security standards and adapted to the unique risks of AI systems. Every engagement ends with a clear checklist and threat model so you know exactly what was tested and why it matters.
For AI and LLM systems, our process includes:
How It Works
Cybersecurity is complex. Your path to enterprise readiness doesn’t have to be.
From Findings to Peace of Mind
You get a report that engineers can act on and leaders can trust.
Representative Findings (anonymized)
Real examples of issues we’ve identified and helped clients fix.
Each one shows the kinds of vulnerabilities that can slip through without focused AI/LLM security testing.
Agent Tool Misuse
Unauthorized Data Access
What we tested:
A support copilot with search and file retrieval tools.
What we did:
Steered the agent into triggering a high-permission tool without checks.
What we found:
Access to invoices and configuration files containing environment variables.
Why it mattered:
Allowed sensitive data exfiltration via “helpful” tool misuse.
Fix implemented:
-
Least privilege for tools
-
Pre-execution guardrails
-
Output sanitization
-
Abuse simulations in testing
RAG Namespace Escape
Cross-Tenant Data Leakage
What we tested:
Multi-tenant knowledge assistant using a shared vector database.
What we did:
Crafted queries exploiting missing tenant filters.
What we found:
Snippets from another tenant’s documents in responses.
Why it mattered:
Violated data isolation, risking regulatory breaches and trust.
Fix implemented:
-
Strict metadata and namespace isolation
-
Per-tenant database collections
-
Application-layer filter enforcement
-
Filter validation in CI/CD
Why Teams Choose Sekurno
Our clients trust us because we go beyond surface checks — we focus on finding what’s truly exploitable and delivering solutions that matter.
Specialized in AI Security
We test the parts that make AI systems fail in production: prompts, RAG pipelines, tools, agents, and data boundaries.
Senior Engineer Expertise
Every engagement is led by seasoned security engineers with real-world experience.
Actionable, Not Noisy
Reports are clear, evidence-based, and prioritized so your team can act fast.
End-to-End Partnership
From testing to retesting, we stay engaged to verify fixes and ensure closure.
100+
Critical Issues Found
$100M+
Saved for our Clients
5/5
Client Satisfaction Rate
90%
Clients return
What Our Clients Say
We felt that Sekurno really checked every bit and piece of our system.
This was evident in the deliverables they provided, with full transparency — including the testing status of each OWASP WSTG requirement and testing logic informed by their own threat modeling.
They were also the only team that advocated for a white-box approach — giving their security engineers deeper visibility into our application’s implementation and design, which can ultimately help with uncovering meaningful issues. It made the entire process feel aligned with how we actually build and operate.
Nima S, CEO, OASYS NOW
