LLM & AI Security Testing
Securing AI Systems and Large Language Models
Chatbots, copilots and AI agents are now handling customer conversations, internal documents and business decisions, and they have created an attack surface that traditional security testing does not cover. A single crafted prompt can make an AI assistant reveal confidential data, ignore its instructions or misuse the tools it is connected to. Xium Labs builds production AI systems as well as testing them, which is exactly why we know where they break. Our LLM and AI security testing service finds these weaknesses before attackers do.
If that AI sits inside a robot, a machine or an embedded product, see robotics and embedded security testing, where the same work is mapped to the Machinery Regulation and Cyber Resilience Act requirements a manufacturer has to evidence.
What is LLM & AI Security Testing?
It is a structured security assessment of applications built on large language models and other AI components. Using the OWASP Top 10 for LLM Applications and MITRE ATLAS as our framework, we attack your system the way a real adversary would: injecting hostile prompts, probing retrieval pipelines for data leakage, abusing agent and tool permissions, and testing the guardrails that are supposed to keep the model in line. The result is a clear, prioritised report showing what an attacker could actually achieve and how to stop them.
Benefits of Our LLM & AI Security Testing.
Built by AI Engineers
Testers who build production LLM systems themselves, so they know exactly where these systems break.
OWASP LLM Top 10 Coverage
Structured testing against the OWASP Top 10 for LLM Applications and MITRE ATLAS techniques.
Protect Data and Reputation
Stop prompt injection and data leakage before they expose customer records or company secrets.
Regulation Ready
Evidence for GDPR, the EU AI Act, NIS2 and DORA conversations with regulators and enterprise buyers.
Free Re-testing
A free re-test within six weeks confirms your fixes actually close the gaps we found.
Common AI and LLM Vulnerabilities
Prompt Injection
Crafted inputs that override system instructions, hijacking the model through direct or indirect attacks.
Sensitive Information Disclosure
Models leaking personal data, credentials or proprietary context from training data or RAG sources.
Insecure Output Handling
Model output passed unchecked into browsers, databases or shells, enabling XSS and injection attacks.
Excessive Agency
Over-permissioned agents and tool integrations that let a manipulated model take damaging actions.
Training Data Poisoning
Manipulated training or fine-tuning data that plants backdoors or biases in model behaviour.
Unbounded Consumption
Inputs engineered to exhaust context windows, rack up API costs or deny service to real users.
How Does AI Security Testing Work?
We assess your AI systems end to end, from the model interface to the data and tools behind it. Our process involves the following steps:
- Scoping & Threat Modelling: We map your AI architecture, data flows, integrations and the realistic attackers it faces.
- Prompt Injection Testing: Direct and indirect injection attacks attempt to override instructions and hijack behaviour.
- Data Leakage Testing: We probe the model, its context and retrieval sources for exposure of sensitive information.
- Agent & Tool Abuse: Connected tools, functions and permissions are tested for actions a manipulated model should never take.
- Guardrail Evaluation: Content filters and safety controls are stress tested against bypass and evasion techniques.
- Reporting & Remediation: A prioritised report with proof of each finding, fixes for your engineers, and a free re-test within six weeks.
LLM & AI Security Testing FAQs
What is LLM penetration testing?
It is a security assessment of applications built on large language models. We attack your system the way a real adversary would: injecting hostile prompts, probing retrieval pipelines, abusing tool integrations and attempting to extract sensitive data, then give you a prioritised plan to fix what we find.
We use OpenAI or Azure. Is security not their responsibility?
The model provider secures the model infrastructure, but your application layer remains your responsibility: your prompts, your retrieval pipeline, your tool permissions, your data. Almost every serious LLM vulnerability lives in that application layer.
What do you actually test?
Chatbots and copilots, RAG pipelines, agent and tool integrations, guardrails and content filters, plus the surrounding infrastructure of APIs, authentication and data stores. We test against the OWASP Top 10 for LLM Applications and relevant MITRE ATLAS techniques.
How long does an engagement take?
Most LLM security assessments run one to two weeks depending on the number of systems and integrations in scope. You receive the full report within a few days of testing finishing.
How often should AI systems be tested?
At least annually, and after any significant change: a new model version, new tools or agents, a new data source in your retrieval pipeline, or a change to system prompts and guardrails.
Can you also test our conventional applications?
Yes. Most clients combine AI security testing with web application, API or network penetration testing so the whole attack surface is covered in one engagement and one report.