As organisations adopt artificial intelligence (AI), we have evolved our testing methodology to assess whether these systems are resilient against targeted attacks.
years testing for government and enterprise
CREST-accredited through CREST ANZ
Our testers have deep experience building, using and attacking AI applications and large language models (LLMs).
Combined with our established penetration testing methodology, that lets us find the weaknesses unique to AI systems, from prompt injection and data poisoning to training data extraction and improper output handling.
A traditional penetration test targets vulnerabilities in software, networks and infrastructure. An AI penetration test applies the same rigorous methodology to the components that make AI systems different. Conventional applications follow deterministic logic; AI models are probabilistic, so the same input can produce different outputs, which creates vulnerability classes standard testing is not designed to find.
Traditional pen testing is like cracking a safe’s lock. AI pen testing is often like convincing the person guarding the safe to hand over the contents.
the model itself
its training data
its integration points
the guardrails that constrain its behaviour
The mindset is closer to social engineering than to traditional exploitation. Successful attacks can bypass safety guardrails, exfiltrate sensitive data processed by the model, or manipulate downstream systems that consume AI-generated output.
AI is still new to enterprise environments. Development teams are integrating AI tools quickly, but secure integration practices are not yet widely established, which leaves gaps that conventional security testing will not detect.
A list of vulnerabilities without context is not actionable. Because AI is non-deterministic, vulnerabilities often sit in the model’s logic and behaviour rather than the code. We find them and map them to the OWASP Top 10 for LLM Applications and MITRE ATLAS, so you understand your actual exposure.
AI security often needs remediation that blends code fixes with AI-specific controls such as prompt engineering, content filtering and output sanitisation. Every finding gets a CVSS v4.0 rating, so your team can fix the most critical AI risks first.
Your policies may say your AI must not generate harmful content or disclose internal information, but do those constraints hold against a motivated attacker? We verify that safety constraints, access controls and content filtering work under adversarial conditions.
Standard vulnerability scanners are not designed to find AI weaknesses. We test prompt injection, data poisoning, improper output handling that triggers flaws such as XSS or command injection downstream, and training data extraction.
These frameworks are the current industry baseline for identifying and categorising security risks in AI and generative AI systems.
The ten most critical risks in LLM applications, including prompt injection, sensitive information disclosure, data and model poisoning, and improper output handling.
Adversary tactics and techniques against AI systems, based on real-world observations and red team demonstrations. We use it to plan and classify AI attack scenarios.
A framework for managing AI risk across the AI lifecycle. Our findings give evidence for its Measure and Manage functions.
Our GRC specialists can align your AI security posture with frameworks including the ACSC Essential Eight and ISO 27001. For AI used to accelerate conventional testing, see AI-augmented penetration testing.
They share similarities, but AI pen testing targets the probabilistic nature of the model itself. As well as standard web vulnerabilities such as broken access controls and injection flaws, we test for logic manipulation, exploitable bias and hallucination behaviours an attacker can use. The method is closer to social engineering than to traditional technical exploitation.
Yes. For commercial AI products, the scope is your configuration, data handling and governance controls rather than the vendor’s core model: we test whether your deployment prevents data leakage, enforces access controls and follows your policies. For custom AI applications and LLM integrations, we test the full stack, including model behaviour, integration points and output handling.
We use a hybrid approach. Automated tooling scales testing of common prompt patterns and known attack vectors, but the most critical findings, such as complex logic bypasses and chained exploitation paths, come from manual testing by our assessors. Automated tools alone cannot replicate the creative, adversarial thinking those findings need.
We align our methodology with the OWASP Top 10 for LLM Applications, the MITRE ATLAS adversarial threat matrix for AI, and the NIST AI Risk Management Framework (AI RMF 1.0).
Any system with AI or LLM components, including customer-facing chatbots, internal knowledge assistants, document processing and summarisation tools, code generation assistants and custom LLM integrations via APIs. The methodology is adapted to the architecture and risk profile of each engagement.
If your organisation is developing or deploying AI, a targeted AI penetration test is the most direct way to verify your implementation is secure before an attacker finds out it is not.
We can scope a test for your AI risks, whether you run a customer-facing chatbot, an internal knowledge assistant or a custom LLM integration.
