AI penetration testing is structured security testing of AI systems, models, APIs, and the infrastructure supporting them. A Theos AI penetration test assesses whether an attacker can manipulate your AI systems, extract sensitive data through them, abuse connected workflows, or use your AI infrastructure as a stepping stone into broader environments.
The attack surface is distinct from traditional application testing. It includes the model layer, the API and prompt interface, training data access controls, agent permissions, third-party integrations, and the identity controls governing who can interact with AI systems and what those systems can be instructed to do.
You know exactly where your AI deployment is exploitable before adversaries discover it. Findings are prioritised by business impact and delivered with clear remediation guidance. Your AI programme continues to scale on a foundation that has been tested.
Across APAC enterprises, AI is being embedded into development pipelines, customer-facing systems, and operational workflows faster than security programmes have adapted. Traditional penetration testing frameworks were built for a different attack surface. Prompt injection, model abuse, and the risks introduced by agentic AI systems require a different testing approach. Adversaries are already exploiting this gap.
Regulators in Singapore and Hong Kong are responding. AI governance expectations are evolving across financial services, with growing attention on AI system integrity, data access controls, and model risk management. For organisations deploying AI in regulated environments, AI security testing across APAC is becoming a supervisory expectation.
Every AI penetration test begins with a scoping review of your AI deployment, the systems in use, the data they connect to, the integrations they support, and the permissions they carry. Testing is prioritised around the components that carry the highest exploitation risk given your specific deployment pattern and sector.
Theos AI penetration testers approach your systems from the perspective of an attacker who has identified your AI deployment as a target. Testing covers the techniques adversaries are actively using, including the prompt injection and model abuse patterns documented in current threat intelligence, not just the vulnerability classes covered by generic security standards.
AI security testing at Theos covers the full deployment stack: model and prompt layer, API surface, agentic workflow behaviour, integration and connector security, access controls, and the infrastructure hosting the deployment. Each layer is assessed because adversaries will probe all of them.
AI penetration test findings are documented with evidence, impact assessment, and remediation guidance specific to your deployment. Findings are prioritised by the business risk each issue creates, not just the technical severity. The debrief is conducted with both your security and AI programme teams because the implications span both.
Know where your AI deployment is vulnerable before it is targeted
Documented AI security testing evidence for financial services regulators and enterprise procurement processes
An AI Security Assessment that validates your AI deployment controls are functioning as intended
Prioritised findings with specific guidance for each issue identified
Findings feed into VAPT scope prioritisation and MDR detection tuning for AI-related attack patterns
Attack surfaces, trust boundaries, and high-risk abuse paths mapped across your AI deployment before testing begins.
Identity and access management, API security, infrastructure hardening, and AI-specific safeguards including input validation, output filtering, and tool restrictions.
Prompt injection, model abuse, data extraction, unsafe output handling, agent and tool abuse, and agentic workflow exploitation across the agreed scope.
Hallucination and misinformation risks, bias and discriminatory behaviour, unsafe content generation, and model output consistency in sensitive use cases.
Findings mapped to OWASP AI, OWASP Top 10 for LLM, and NIST AI RMF. Debrief with your security and AI teams. Executive summary for leadership and regulatory review.
Theos AI penetration testers work from current threat intelligence on how adversaries are targeting AI systems in live deployments. Testing is built around the techniques documented in the field, not generic vulnerability checklists that do not reflect how AI attacks actually unfold.
Most security teams approach AI testing through a single lens, either model security or application security. Theos tests the full stack: model, API, agent, integration, access control, and infrastructure. Adversaries do not limit themselves to one layer and neither do we.
AI penetration test findings feed directly into your vulnerability management programme, MDR detection tuning, and VAPT scope prioritisation. Clients who work with Theos across multiple service lines benefit from intelligence that compounds across engagements.
Theos holds CREST accreditation across our offensive security practice. AI penetration testing engagements are conducted to CREST standards for methodology, engagement process, and delivery. CREST does not yet publish an AI-specific accreditation framework. Our accreditation confirms the rigour of how we work.
Your AI deployment may be functioning exactly as designed. A Theos AI penetration test tells you whether it is also secure. No assumptions. Practitioner-led testing that finds what adversaries will find.
We deliver outcomes.
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