Attackers don't hack your LLM. They just ask nicely.
Your model makes real decisions: approvals, diagnoses, fraud calls.
It was tested to be accurate, but was it tested to resist an attacker who feeds it poisoned data?
We test whether it can be fooled.
It was tested to be accurate, but was it tested to resist an attacker who feeds it poisoned data?
We test whether it can be fooled.
The question that matters
Everyone checks your model is accurate. But has anyone checked whether it can be poisoned, stolen, or tricked into making a costly mistake?
WHY MOST AI SECURITY TESTINGs FALL SHORT
AI security is not an extension of app security.
Most teams test AI for accuracy and speed, and barely at all for security. They miss adversarial inputs, poisoned training data, and model theft - the attacks that turn a working model against you.
These need ML expertise, not a generic scanner.
These need ML expertise, not a generic scanner.
Most AI Security Testing Vendors
Tests the model for accuracy and speed
Little or no adversarial robustness testing
Assumes the training data is safe
Rarely checks for model theft
Overlooks LLM prompt injection
A security team with limited ML knowledge
Payatu
AI-native and research-led: adversarial testing and robustness validation
Comprehensive AI threat modelling
Data poisoning and backdoor testing
Model extraction and IP-protection testing
LLM security and prompt-injection testing
Dual expertise: security researchers and ML specialists
What we test
The whole AI stack
We scope to the AI systems you actually run, then test every layer a real attacker would.
LLM & GenAI apps
RAG pipelines
AI agents & tool use
Computer-vision models
Fraud & recommendation models
MLOps & training pipelines
Model APIs & endpoints
Third-party & open-source models
LLM & GenAI apps
RAG pipelines
AI agents & tool use
Computer-vision models
Fraud & recommendation models
MLOps & training pipelines
Model APIs & endpoints
Third-party & open-source models
Models
Adversarial inputs that fool the model
Data poisoning & backdoors
Model theft & extraction
Model inversion (leaking training data)
Models
Gen AI
Prompt injection & jailbreaks
Data leakage through the model
Unsafe or harmful outputs
Agent actions & tool use
Gen AI
Infrastructure
Model serving APIs & endpoints
Training data & pipelines (MLOps)
Third-party models & datasets
Access, authentication & rate limiting
Infrastructure
Models
Adversarial inputs that fool the model
Data poisoning & backdoors
Model theft & extraction
Model inversion (leaking training data)
Gen AI
Prompt injection & jailbreaks
Data leakage through the model
Unsafe or harmful outputs
Agent actions & tool use
Infrastructure
Model serving APIs & endpoints
Training data & pipelines (MLOps)
Third-party models & datasets
Access, authentication & rate limiting
Process
How we audit.
Simple, step by step, from scoping your AI to confirming the fix.
Scope
You tell us what your AI does and what it decides. We plan the test around it.
01
Threat modelling
We map how your AI could be attacked: poisoned, tricked, leaked, or stolen.
02
Reporting
Every finding in plain language, with proof and business impact.
04
Security testing
We attack the model, the LLM, and the systems around it, by hand and with our own tools.
03
Remediation
guidance
Clear fixes for your data scientists and engineers, in your stack.
05
Re-test
We check every fix and confirm the risk is closed.
06
Process
How we audit
Simple, step by step, from scoping your AI to confirming the fix.
Scope
You tell us what your AI does and what it decides. We plan the test around it.
01
Threat modelling
We map how your AI could be attacked: poisoned, tricked, leaked, or stolen.
02
Security testing
We attack the model, the LLM, and the systems around it, by hand and with our own tools.
03
Reporting
Every finding in plain language, with proof and business impact.
04
Remediation guidance
Clear fixes for your data scientists and engineers, in your stack.
05
Retest
We check every fix and confirm the risk is closed.
06
Process
How we audit.
Simple, step by step, from scoping your AI to confirming the fix.
Scope
You tell us what your AI does and what it decides. We plan the test around it.
01
Threat modelling
We map how your AI could be attacked: poisoned, tricked, leaked, or stolen.
02
Security testing
We attack the model, the LLM, and the systems around it, by hand and with our own tools.
03
Reporting
Every finding in plain language, with proof and business impact.
04
Remediation guidance
Clear fixes for your data scientists and engineers, in your stack.
05
Retest
We check every fix and confirm the risk is closed.
06
Testimonials
What AI teams say about working with us
Payatu's focus on in-depth defence, quality, and proactive approach to all their services were precisely what our fast-growing publicly listed company needed.
Payatu's Services have helped us in ensuring that not only do we exceed strict compliance standards, but also ensure that security is not just a tick box exercise in our organisation. We have been able to make security an integral part of...
Payatu's Services have helped us in ensuring that not only do we exceed strict compliance standards, but also ensure that security is not just a tick box exercise in our organisation. We have been able to make security an integral part of...
Payatu delivered a 360-degree penetration testing exercise across our web applications and internal network. Their structured, methodical approach and deep technical understanding were evident throughout the engagement. They didn’t just give us a list of vulnerabilities, they provided actionable insights that helped to improve our security posture. The engagement was constructive.
WHY PAYATU
Why AI teams pick us
AI security isn't an extension of app security. We bring the adversarial ML expertise most
security teams don't have.
security teams don't have.
Contributors to OWASP ML Top 10 & MITRE ATLAS
ISO 17025 accredited · CERT-In empanelled
Founders of Nullcon & hardwear.io
Research at Black Hat & DEF CON AI Villages
Security researchers + ML specialists
Published work on adversarial ML & LLM flaws
What you get
The whole story, not just an accuracy score.
Complete explaination
Exactly what we found and how, across your models, LLMs, and pipelines, in plain words.
What it means for the business
Each issue tied to real impact: bad decisions, data leaks, or a stolen model.
The proof
Working attacks, crafted inputs, and evidence for every finding. Reproducible by your team.
Model hardening & re-test
Fixes for your data scientists and engineers, monitoring advice, and a retest to confirm.
Want to get your AL ML security assessed?
Talk to a security researcher and discuss the security posture of your company
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