AI Security · New

AI / LLM Security Assessments

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 partner Lorikeet Security.

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Real attacks, not regex scans

We 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.

01

Prompt injection & jailbreak testing

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.

02

Data leakage & PII exposure

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.

03

Agent abuse & tool misuse

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.

04

Model integrity & supply chain

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.

What an engagement covers

A typical engagement runs 2 to 4 weeks depending on scope. Below is what we hand back.

StandardOWASP LLM Top 10
FormatExecutive + technical reports
FindingsRanked by exploitability
Re-testIncluded on remediation
Delivery2–4 weeks typical
ConfidentialityNo prompts shared with vendors

Why this team, why now

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.

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Lorikeet Security partnership

Lorikeet Security runs adversarial testing across CTF, red team, and AI agent assessments. They host live events with thousands of participants and have sponsored DEF CON and BSides. We bundle their hands-on testing with our remediation strategy. One engagement, one invoice.

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Published CVEs and AppSec lineage

Our founder 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.

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Strategy that survives the report

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.

How we work

Three phases. No surprises on scope or invoice.

01

Threat-model the surface

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.

02

Adversarial testing

Lorikeet Security 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.

03

Remediate & re-test

Findings ranked by exploitability with concrete fixes. We help your engineers ship the patches, then re-test the high-severity items before sign-off.

Works well with

Ship AI features without shipping a breach

Tell us what you are building. We will scope an assessment in one call.

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Frequently asked questions

What does an AI/LLM security assessment cover?

We 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.

How is this different from a normal pen test?

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.

Do you test agents and tool-using systems?

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.

Will this help with enterprise AI security reviews?

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.

Who runs the assessment?

It is led by our founder, a published security researcher with 6 CVEs. Deeper application and infrastructure penetration testing is delivered with our partner Lorikeet Security when scope calls for it.