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    AI Security Testing

    What is AI Systems Penetration Testing?

    A prompt is not a permission layer. AI systems penetration testing is a specialized security assessment for the LLMs, chatbots, coding agents, and MCP integrations your business actually runs. We test the attack surfaces that traditional pentesting frameworks miss: prompt injection, data leakage, over-scoped agent permissions, bypassable guardrails, and secret keys exposed by fast, unvalidated builds.

    Focused, context-aware, and human-led. Coding agents and MCP servers now hold real access to your code, your CRM, and sometimes your production database. We test them the way an attacker would, assuming that anything an agent can read or touch, it eventually will. Findings stay relevant for EU AI Act Article 15 conformity, with auditable evidence for your compliance team.

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    What is the testing scope?

    Direct and indirect prompt injection testing
    Tool-call and MCP server abuse
    Privilege escalation and excessive agency via the AI layer
    Tenant isolation in multi-tenant SaaS
    Data extraction and leakage through AI interfaces
    Model manipulation, RAG poisoning, and guardrail bypass
    Resource-consumption and availability abuse
    Secret and credential exposure in AI-enabled apps

    How do we approach AI testing?

    Prompt Analysis

    We test whether prompts, context manipulation, or hidden instructions can steer the model beyond its intended behaviour, the way a real user or attacker would.

    Guardrail Testing

    We deliberately bypass the safety rules and restrictions in place, showing exactly where guardrails fall short.

    Agent and MCP permissions

    We map what your agents and MCP servers can actually reach, then test scoped keys, tool-call abuse, and permission bypasses. A prompt is not a permission layer, so we attack the access, not the instructions.

    Data Leakage

    We check whether sensitive data can leak through model responses, memory, or connected integrations, directly or indirectly.

    Practitioners who use AI themselves

    Our ethical hackers build and run their own AI research stack, validated at the Meta Bug Bounty Research Conference 2026 in Taipei. We are active practitioners, not observers.

    What does EU AI Act Article 15 require?

    High-risk AI systems under Annex III of the EU AI Act must meet cybersecurity requirements. The Digital Omnibus agreement of 7 May 2026 moved the compliance deadline from 2 August 2026 to 2 December 2027 (stand-alone Annex III) and 2 August 2028 (Annex I, embedded in regulated products). A structured AI pentest is the most direct path to documented Article 15 compliance.

    What is a high-risk AI system?

    Annex III covers AI used for hiring, credit scoring, biometrics, critical infrastructure, and education. If your system falls under it, security testing under Article 15 is a legal obligation.

    Article 15: what it requires

    High-risk AI must resist adversarial attempts to alter its output or behaviour. A structured AI pentest gives you documented proof that it does.

    Deadline: 2 December 2027

    The Digital Omnibus of 7 May 2026 moved the deadlines to 2 December 2027 (stand-alone Annex III) and 2 August 2028 (AI in regulated Annex I products). Start early to leave room for fixes and retests.

    Audit evidence for conformity

    Findings are mapped to Article 15, giving your compliance team and conformity assessment body auditable proof that the security obligations are met.

    How does an AI pentest differ from a classic pentest?

    AI security testing and classic application penetration testing overlap in some areas and diverge in others. Understanding the difference helps you scope the right assessment for your situation.

    What's the same

    Both assess how an application handles malicious input: authentication, API security, authorisation logic, injection, and data exposure.

    What's unique to AI

    Prompt injection, context window manipulation, training data extraction, jailbreaks, guardrail bypass, and tool-call abuse exist only in AI systems. Standard methodology does not cover them.

    Why you need both

    AI embedded in a web app inherits the risks of both layers. We combine AI testing with application or API testing for full coverage and one evidence package.

    Frequently Asked Questions

    We test LLM-based apps, chatbots, AI assistants, ML-driven features, and AI integrations including retrieval-augmented generation (RAG), tool-calling, and multi-agent workflows.

    Yes. We test the AI agents and integrations your organisation runs internally, focused on what they can access and how that access could be abused: MCP server permissions, scoped keys versus prompt-based guards, hook and validation bypasses, and secret exposure in fast-built applications.

    No. A prompt is a suggestion, not a security boundary. An agent can route around an instruction, including by writing and running a script to do something it was told not to do directly. Real controls live in scoped permissions and restricted credentials, which is exactly what we test.

    Yes. For SaaS products we focus on what matters most when an AI assistant acts on a user's behalf: tenant isolation, authorization and rights checks inside the assistant, privilege escalation through the AI layer, excessive agency, unauthorized or unintended tool calls, and resource-consumption abuse that could affect platform stability. The AI should only ever act within the permissions of the user invoking it, and we verify that this holds under attack.

    Yes. We assess direct and indirect prompt injection, instruction override, jailbreaks of system prompts, RAG poisoning, model confusion, and guardrail bypass paths, including tool-call abuse and data exfiltration through AI features. We work from the OWASP Top 10 for LLM Applications and the OWASP Top 10 for Agentic Applications (2026), extended with context-specific attack chains, and every finding is manually validated by our ethical hackers rather than AI-only scanning.

    Yes. High-risk AI systems under Annex III of the EU AI Act must comply with Article 15 cybersecurity requirements by 2 December 2027 (stand-alone systems) or 2 August 2028 (AI embedded in regulated products under Annex I), following the Digital Omnibus agreement of 7 May 2026. Our AI penetration test produces findings mapped to Article 15 obligations, giving your compliance team auditable evidence of conformity.

    Yes. We test for unintended disclosure of sensitive data via model outputs, memory, retrieval sources, file and tool access, and other AI interfaces.

    Absolutely. Many AI risks live in the surrounding application, APIs, authentication, and integrations. We can combine both assessments for a complete view and a single unified evidence package.

    Assess your AI security posture

    Get a comprehensive view of your AI system vulnerabilities and EU AI Act compliance readiness.

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