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AI Penetration Testing
Artificial Intelligence (AI) and Large Language Models (LLMs) are transforming how enterprises operate, innovate, and interact with data. As organizations build, train, and deploy AI driven systems, security teams face a rapidly evolving challenge: ensuring that these models — and the systems they connect to — remain secure, trustworthy, and resilient against adversarial manipulation.
AI and LLMs represent a fundamentally new class of software with unique attack surfaces. They can be influenced through crafted prompts, manipulated into leaking sensitive information, or exploited through insecure integrations and model driven logic flaws. These risks fall outside the scope of traditional penetration testing and require specialized adversarial techniques to properly evaluate and mitigate.
Canary Trap’s AI Penetration Testing service provides structured, model aware offensive testing designed to uncover vulnerabilities across the full AI stack. Our assessments identify weaknesses in prompt handling, data retrieval, model behavior, system integrations, and operational safeguards — delivering the assurance needed to deploy AI responsibly and securely.
Why AI and LLM Penetration Testing Matters:
- AI introduces new attack surfaces. Models can be coerced through prompt injection, jailbreaks, or adversarial inputs that bypass intended controls.
- LLMs often have access to sensitive systems. When models can read documents, query APIs, or trigger actions, a malicious prompt can misuse those capabilities.
- AI systems may leak confidential data. Poorly configured retrieval, memory, or fine tuning pipelines can expose internal or sensitive information.
- Traditional testing does not cover model specific risks. AI behaves differently from web applications or APIs, requiring specialized testing methodologies.
- Regulators increasingly expect AI security validation. Frameworks such as NIST’s AI Risk Management Framework emphasize adversarial testing as a core component of responsible AI deployment.
Our Methodology
Canary Trap adheres to the OWASP AI Testing Guide, a standardized framework for evaluating the trustworthiness and security of AI and LLM based systems. Our testing approach includes repeatable, adversarial test cases across:
- AI Application Layer – User interfaces, prompt handling, guardrails, and business logic
- AI Model Layer – Model behavior, jailbreak susceptibility, and adversarial manipulation
- AI Infrastructure Layer – APIs, integrations, orchestration layers, and access controls
- AI Data Layer – Training data, retrieval systems, memory features, and data leakage risks
Our Report of Findings provides a prioritized view of vulnerabilities, mapped to risk, exploitability, and potential business impact — enabling your team to strengthen the trustworthiness and security of your AI systems with confidence.
Canary Trap combines human expertise with sophisticated tools, proven methodologies and, where appropriate, threat intelligence to ensure a thorough, in-depth approach to security testing and assessments.
For more information, please complete our Scoping Questionnaire or Contact Us.
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FAQs
What is AI penetration testing?
AI penetration testing is a specialized adversarial assessment focused on identifying vulnerabilities in AI and LLM based systems. It evaluates how models respond to malicious prompts, how they interact with connected systems, and whether they can be manipulated, misled, or coerced into unsafe behavior.
How is AI penetration testing different from traditional penetration testing?
Traditional penetration testing evaluates applications, networks, and APIs. AI systems introduce additional risks — such as prompt injection, jailbreaks, data leakage, and model driven logic flaws — that require model aware testing techniques. AI behaves probabilistically, not deterministically, so the threat landscape is fundamentally different.
Why do AI and LLMs require specialized security testing?
AI models can be influenced through crafted inputs, manipulated into revealing sensitive information, or exploited through insecure integrations. Because these risks are unique to AI, they fall outside the scope of conventional security assessments and require targeted adversarial testing.
Is AI penetration testing safe for production systems?
Yes. Canary Trap uses controlled, non destructive testing techniques aligned with the OWASP AI Testing Guide. All activities are coordinated with your technical stakeholders to ensure safe execution without disrupting model performance or connected systems.
What types of vulnerabilities can AI penetration testing uncover?
Assessments can identify issues such as:
- Prompt injection and jailbreak susceptibility
- Data leakage through retrieval or memory systems
- Insecure API or system integrations
- Model hallucinations that create operational risk
- Logic flaws in model‑driven workflows
- Weak or ineffective guardrails
Do you test both custom models and third party LLMs?
Yes. Canary Trap evaluates proprietary models, fine tuned models, retrieval augmented systems, and third party LLM integrations. This includes both on premise deployments and cloud hosted AI services.
What frameworks guide your testing methodology?
Our assessments follow the OWASP AI Testing Guide, which provides standardized, repeatable test cases across the AI Application, Model, Infrastructure, and Data layers. This ensures comprehensive coverage and alignment with emerging industry expectations.
Can AI penetration testing help with regulatory or audit requirements?
Yes. Regulatory bodies and industry frameworks — including NIST’s AI Risk Management Framework — increasingly emphasize adversarial testing as part of responsible AI deployment. Our findings help organizations demonstrate due diligence and strengthen governance.
What deliverables will we receive?
You will receive a detailed Report of Findings that includes:
- A prioritized list of vulnerabilities
- Risk ratings and potential business impact
- Evidence and technical detail for each issue
- Recommendations to improve AI trustworthiness and security
How often should AI penetration testing be performed?
Most organizations test AI systems whenever models, integrations, or data pipelines undergo significant changes. Rapid model evolution and shifting threat activity often justify more frequent assessments.
