Cyber Security Services- Securing Fortune 100 companies since 2014
AI features change how an application can be attacked. A chatbot, copilot or AI agent can be talked into ignoring its instructions, leaking data from the documents it can read, or taking actions it should never take. Our AI penetration testing attacks your AI applications the way real adversaries do, following the OWASP Top 10 for LLM Applications and MITRE ATLAS, and tests the application, APIs and integrations around the model too. It complements our AI security services and is part of our penetration testing services.
We map your AI application: the model and provider, system prompts, data sources, tools and integrations, user roles, and what a successful attack would look like for your business.
We combine curated attack libraries with hands-on testing by our penetration testers, because the most damaging AI attacks are creative and context-specific.
We test the APIs, authentication, data retrieval and downstream systems the AI touches, where an AI weakness becomes a real breach.
You receive an executive summary and a technical report mapping every finding to the OWASP Top 10 for LLM Applications and MITRE ATLAS, with example prompts, impact and specific fixes.
We help your team strengthen prompts, guardrails, permissions and output handling, then retest to confirm the fixes hold.
Schedule a scoping call to plan penetration testing for your chatbot, copilot, RAG application or AI agents.
Tell us what you need tested and we will send a fixed-price quote within one business day. Prefer to talk? Book a scoping call.
AI penetration testing is a security assessment of applications that use large language models or other AI, such as chatbots, copilots, retrieval-augmented generation (RAG) apps and AI agents. Testers try to make the AI ignore its instructions, leak data, misuse its tools or harm downstream systems, and report how to fix what they find.
A web application test looks for flaws in code and access control. AI applications add new attack surfaces in natural language: prompt injection, data leakage through retrieval, and agents with too much authority. We test both, because AI weaknesses usually become serious through the application and integrations around the model.
The OWASP Top 10 for LLM Applications and MITRE ATLAS for adversarial techniques, with findings that also support AI governance programs built on the NIST AI Risk Management Framework and ISO/IEC 42001.
Yes. Agents that can call tools, send messages or change data are one of the highest-risk areas, and we test what an attacker can make them do, including through Model Context Protocol (MCP) servers and plugins.
For commercial AI assistants, the most valuable work is testing how they are configured in your environment, such as what data Copilot can reach through Microsoft 365 permissions and whether oversharing exposes sensitive files. We scope that with you, alongside our AI security assessments.
Pricing depends on the number of AI features, models, data sources and integrations in scope. We provide a fixed quote after a free scoping call.
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