A fixed-price gap analysis, remediation through to your audit, and upkeep after it. All in a workspace you keep.
All compliance →Led by a published security researcher with five CVEs. One standard report, letters for your buyers, and retests of your fixes.
All security →To the people you sell to, raise from or answer to.
All industries →Original research, free tools, and plain-language guides on security and compliance, from a published security researcher.
Read the blog →Every startup is shipping AI features. Almost none have tested them for prompt injection, data leakage, or agent abuse. We do. Strategy comes from traztech. Adversarial testing comes from our offensive-security partner.
Book a scoping callWe treat your AI feature like an attacker would. Hand-crafted prompt corpora, multi-turn jailbreaks, and live agent abuse. Not a wrapper over an open-source scanner.
Direct and indirect prompt injection. Role override and system-prompt extraction. Multi-turn jailbreaks that defeat naive guardrails. Tested across the OWASP LLM Top 10 plus our own corpus from real engagements.
RAG poisoning, context-window leakage, training-data exfiltration, cross-tenant context bleed. We try to make your model surface another customer's data. If it can be done, we will find the path.
Autonomous agents acting on attacker-controlled inputs are the new SSRF. We test for tool-call injection, side-effect chains, privilege confusion across tools, and unbounded action loops. Critical for any agent that touches a database, an API, or a billing system. If you have not shipped the agent yet, testing it before launch is the cheapest time to find these.
Base-model provenance, fine-tune dataset audit, third-party API trust surface, and model serialization risks. Most teams forget that a hosted model is a third-party dependency with a privileged seat in their stack.
The testing window is agreed at scoping. Below is what we hand back.
AI security is a new label on an old discipline. The teams that win at it are the ones that have already done AppSec the hard way.
Adversarial testing is delivered with our testing partners, who run penetration testing, red team, and AI agent assessments. We work with both Canadian and global firms and put the one that gives you the best value on your engagement, and we bundle their hands-on testing with our remediation strategy in one engagement.
Our principal has published CVEs and led application-security programs for years before LLMs were called LLMs. Prompt injection is a parsing problem; data leakage is an authorization problem. We have been doing the underlying work for a decade.
Most pentest reports get filed and forgotten. Ours come with a remediation plan, a re-test, and an optional retainer to keep your AI surface tested as it changes. The report is a starting point, not a deliverable.
Three phases. No surprises on scope or invoice.
Map the AI feature: model, prompts, retrieval sources, tool calls, downstream effects. Identify trust boundaries and the failure modes that actually matter for your business.
Our partner runs the assessment against the threat model with both black-box and grey-box methods, covering prompts plus tooling. We sit in the loop to flag findings against your actual architecture, not generic boilerplate.
Findings ranked by exploitability with concrete fixes. We help your engineers ship the patches, then re-test the high-severity items before sign-off.
Tell us what you are building. We will scope an assessment in one call.
Book a CallWe assess the risks specific to systems that use large language models: prompt injection, insecure output handling, data leakage through prompts and context, model and supply-chain risks, excessive agency in tool-using agents, and access control around model endpoints. The work is mapped to the OWASP Top 10 for LLM applications.
A traditional pen test targets your network, infrastructure, and application logic. An AI assessment targets the model layer: how prompts, context, tools, and outputs can be abused. The threat model is different. We often run both, since an LLM feature still sits on top of conventional infrastructure that needs testing too.
Yes. Agentic systems that call tools, browse, or take actions carry extra risk because a successful injection can trigger real-world effects. We review tool permissions, sandboxing, human-in-the-loop checkpoints, and how untrusted content flows into the agent.
Yes. Buyers increasingly ask how you secure AI features before they sign. A documented assessment with prioritized findings and remediation gives you evidence to answer those questions and shortens the review. It also feeds directly into SOC 2 and broader security questionnaires.
It is led by our principal, a published security researcher with 5 CVEs. Deeper application and infrastructure penetration testing is delivered with our offensive-security partner when scope calls for it.
Free PDFs, no card
The template set as PDFs, including the vendor security questionnaire that most AI vendor reviews start from. Free, no card.
From Jacob Masse, principal of traztech: the files by email, then a few short notes over the next month. No spam, unsubscribe in one click.
Track record
We would rather show you the work than a wall of logos. Here is what is behind the advice.
Five published CVEs. CVE-2024-45163 (CVSS 9.1) is a flaw in the Mirai botnet itself, which gave defenders a way to shut down attacker infrastructure. CVE-2026-42626 takes HP ENVY 5000 printers offline from any unauthenticated device on the same network.
The printer is the one that matters on a compliance page: an asset nobody counts as a computer, on a flat network, downed by a device that never had to log in. Auditors ask how controls fail. We have found out first-hand.
At Humera, a venture-backed US security company, Jacob built the compliance programme in-house from nothing: no report, no policies, no documented controls. It ended in a Type II attestation with zero exceptions.
The platform stayed in production throughout, which is the part most readiness projects get wrong: controls are easy to design and hard to retrofit onto a system people already depend on.