AI & LLM Penetration Testing Explained: A Practical Guide to AI Security Testing

Large Language Models (LLMs) and Generative AI are now embedded across organisations, powering chatbots, copilots, analytics tools, and automated decision-making systems. These technologies are transforming productivity and capability, but they also introduce security risks that traditional cyber security approaches were never designed to address. 

What’s changed is the urgency. According to recent research, 73% of organisations have already deployed or are actively piloting AI solutions. And the incidents that have followed aren’t the work of sophisticated nation-state threat actors. They’re the result of regular people having conversations, discovering that AI systems can be manipulated through simple prompts. The entry barrier to exploiting an AI system isn’t a toolkit; it’s a chat window. 

Unlike conventional software, LLMs don’t behave in a predictable or deterministic way. Their outputs depend on probabilities, context, and user input, which means the same system can behave very differently under slightly altered conditions. This shift creates new attack paths that sit entirely outside the scope of traditional application or infrastructure penetration testing. 

As AI adoption accelerates, security leaders are under growing pressure to understand what these risks mean in practice, how attackers are exploiting them right now, and what good security testing looks like in an AI-driven environment. 

In this guide, we’ll walk you through what LLM and GenAI penetration testing is, why it matters, how it works in practice, and what real-world AI attacks look like based on scenarios DigitalXRAID’s testing team has executed in live engagements. We’ll also cover the OWASP LLM Top 10, secure-by-design controls, and how often your AI systems should be tested.  

Get a clear picture of where your AI security risks sit within your wider cyber strategy, and how specialist testing helps you adopt AI safely and responsibly. 

Key Takeaways

• LLM and GenAI systems introduce security risks that traditional penetration testing doesn’t cover 
• AI security attacks don’t require sophisticated techniques — real incidents have been executed by regular people using simple prompt manipulation 
• LLM penetration testing focuses on prompt manipulation, data leakage, agent misuse, RAG poisoning, and AI-specific attack paths 
• Low-code and no-code AI applications (PowerApps, Make.com, Copilot Studio) carry the same security risks as enterprise-built systems and are frequently overlooked 
• Organisations deploying AI must consider security, compliance, and governance together 
• Specialist testing helps you adopt AI safely without increasing cyber or regulatory risk 

What is LLM & GenAI Penetration Testing?

LLM & GenAI penetration testing assesses security through the eyes of a real attacker, focusing specifically on vulnerabilities unique to AI-driven systems, including large language models (LLMs), AI agents, and LLM-powered applications. 

Unlike conventional application testing, this service evaluates: 

• LLM behaviour and decision-making 
• Prompt handling and manipulation risks 
• AI agent permissions and autonomy 
• Model integrations, APIs, and downstream systems 
• Retrieval augmented generation (RAG) pipelines 
• Third-party models, plugins, and data sources 
• Low-code and no-code AI applications and workflows 

How LLM Penetration Testing Differs from Traditional Penetration Testing

Traditional penetration testing methodologies were built for deterministic systems. If an input goes in, a predictable output comes out. LLMs break that assumption. 

AI systems respond differently depending on phrasing, context, language, and sequencing. This means risks such as prompt injection, jailbreaks, and output manipulation can’t be reliably identified using automated scanners or conventional test scripts. 

Effective LLM penetration testing focuses on model behaviour as well as infrastructure. It assesses how the AI interprets instructions, how guardrails can be bypassed, and how the model interacts with tools, APIs, and downstream systems. 

To do this consistently and rigorously, modern LLM penetration testing aligns with the OWASP Top 10 for Large Language Model Applications, which defines AI-specific risk categories that don’t exist in traditional web or application security testing. Using an OWASP-aligned approach ensures testing is grounded in recognised threat models for LLMs, while still allowing testers to adapt techniques based on how each AI system is implemented in practice. 

What Types of Systems Does LLM & GenAI Testing Apply to?

LLM and GenAI penetration testing is relevant anywhere AI is used to process inputs or influence decisions, including: 

• AI chatbots and virtual assistants 
• Internal copilots and decision-support tools 
• LLM-powered applications and APIs 
• AI agents and automated workflows 
• Low-code and no-code AI applications (including PowerApps, Make.com, n8n, and Microsoft Copilot Studio — tools that allow employees to build AI-powered workflows without security team oversight) 
• RAG-augmented pipelines connected to document repositories and knowledge bases 

If an AI system can access data, trigger actions, or influence users, it represents a potential attack surface that attackers could exploit. 

Why AI Systems Are Being Targeted Right Now: Real-World Incidents

AI security risks aren’t theoretical. They’re being exploited today, often by people with no technical background, using nothing more than a chat interface. Here are four incidents that illustrate the current landscape and the direct business consequences of deploying AI systems without adequate security testing. 

Chevrolet: Prompt Injection and Business Logic Bypass

In December 2023, a Chevrolet dealership deployed a customer service chatbot. Within hours, a user had manipulated it into agreeing to sell a vehicle for one pound through prompt injection, overriding the system’s intended behaviour through natural language alone.  

While more embarrassing than financially catastrophic in this case, the incident illustrates what happens when a chatbot has authority to make commitments without appropriate guardrails. Apply the same vulnerability to a system authorised to process transactions, approve refunds, or access customer data, and the consequences change entirely. 

Samsung: Proprietary Source Code Leaked via Shadow AI

In April 2023, Samsung employees used ChatGPT to help with work tasks including debugging code and summarising meetings. In doing so, they inadvertently leaked proprietary source code and confidential meeting notes. Samsung’s response was to ban ChatGPT entirely across the company.  

This is the practical consequence of shadow AI without governance controls: Employees make their own decisions about which tools to use, and what data to put into them. 

Air Canada: Hallucination Creates Legal Liability

In February 2024, Air Canada’s chatbot gave a customer incorrect information about bereavement fare policies. When the customer attempted to claim the refund, Air Canada argued the company wasn’t responsible for what its AI said.  

The tribunal disagreed. Air Canada was held legally liable for the AI’s output. This case established important legal precedent: if your organisation runs a customer-facing AI system, you’re accountable for what it says and does, regardless of whether a human was directly involved. 

DPD: Chatbot Manipulation and Reputational Damage

DPD’s customer service chatbot was manipulated into swearing and publicly criticising the company. Screenshots went viral. The technical barrier was zero. The reputational damage was immediate and international.  

This wasn’t a sophisticated attack on an enterprise system. It was a conversation. 

The consistent theme across all four incidents: none of these required APT-level tradecraft, exploits, or specialist knowledge. They were executed by regular people having conversations.  

The question for any organisation running AI systems isn’t whether this can happen. It’s whether you’d know about it before it reached the news. 

Why LLM & GenAI Systems Introduce New Cyber Risks

AI systems change the nature of cyber risk by introducing unpredictability and new forms of interaction. Understanding why these risks exist is key to managing them effectively. 

The Shift from Deterministic Software to Probabilistic AI

LLMs generate responses based on probability, not fixed logic. This means they can produce unexpected or inconsistent outputs, even when guardrails are in place.  

Security controls that work well for traditional applications don’t always translate to AI. Input validation, for example, becomes far more complex when the system is designed to interpret natural language flexibly. 

Why Attackers Are Actively Targeting AI Systems

AI security is still a relatively immature discipline. Many organisations deploy AI tools quickly, often without fully understanding the security implications. At the same time, AI systems often sit in front of valuable assets including sensitive data, internal systems, and decision-making processes.  

This combination of low maturity and high reward makes them an attractive target. 

Common LLM & GenAI Security Risks Organisations Face

The OWASP LLM Top 10 is the industry-standard framework for AI security risk, equivalent in importance to the OWASP Web Top 10 for application security. It defines the ten most critical vulnerability categories specific to large language model applications and is the foundation of how DigitalXRAID structures its AI security assessments.  

Here are the risks we see most frequently in real-world deployments: 

Prompt Injection and Jailbreaking (LLM01)

Prompt injection is the highest-priority risk in the OWASP LLM Top 10 and the attack vector we encounter most frequently. It occurs when an attacker crafts inputs that override the AI’s intended behaviour, either directly through user prompts (direct injection) or indirectly through external content the model processes, such as documents, emails, or websites pulled into a RAG pipeline (indirect injection). 

Jailbreaking techniques, including “Do Anything Now” (DAN) prompts and similar prompt engineering approaches, attempt to bypass content restrictions and safety controls. These attacks are often subtle and can be difficult to detect without targeted testing. 

Sensitive Data Leakage and Model Disclosure (LLM06)

LLMs can be manipulated into revealing sensitive information, including personal data, proprietary business information, or internal system prompts. This risk is particularly acute where models have access to internal knowledge bases, document repositories, or customer data. 

System prompt extraction is a specific and serious variant: your system prompt is essentially your security blueprint. It defines what the AI is permitted to do, what data it can access, and what thresholds trigger escalation or refusal.  

Once an attacker has extracted it, they know exactly what controls are in place and how to bypass them. In real engagements, extracted prompts have revealed API endpoints, database schemas, and hardcoded API keys. 

Excessive AI Agent Permissions (LLM08)

AI agents are often granted broad permissions to perform tasks autonomously. If these permissions aren’t carefully scoped, attackers may be able to abuse the agent to perform unauthorised actions, including accessing systems, modifying data, or triggering workflows without oversight. 

In live testing engagements, DigitalXRAID’s team has successfully manipulated AI agents into sending company-wide emails, approving fake expense reports, and granting unauthorised system access.  

None of these required code or technical exploitation. They were achieved through conversation. 

Training Data Poisoning and RAG Vulnerabilities (LLM03)

Training data, fine-tuning datasets, and vector databases can all be targeted for poisoning attacks. These attacks aim to introduce bias, backdoors, or malicious behaviour into the model’s outputs. 

In RAG environments, the risk is immediate and practical: if someone uploads a malicious document to a SharePoint repository that feeds your AI’s knowledge base, the AI may read and follow the attacker’s embedded instructions when queried. This is indirect prompt injection at scale, and it’s particularly difficult to detect because the malicious input arrives through a trusted channel. 

Insecure Plugin Design and Integration Risks (LLM07)

LLMs rarely operate in isolation. Customer service chatbots typically have CRM access, can look up orders, and may be able to process refunds. Internal AI assistants may have access to HR systems, document stores, or financial data.  

Each integration is a potential attack surface. Testing should evaluate whether AI outputs can be abused to exploit these downstream components, including triggering unintended actions or injecting malicious payloads into connected systems. 

Hallucinations, Misinformation, and Business Risk

LLMs can generate confident but incorrect responses. In business contexts, this can lead to poor decisions, regulatory breaches, or reputational damage, as the Air Canada case demonstrates.  

While hallucinations aren’t always a security flaw, they become a risk when outputs are trusted or acted upon without validation. 

Supply Chain Vulnerabilities (LLM05)

Most organisations are using AI through third-party models rather than building their own. Do you know what security measures your AI vendor has in place? What happens when the model is updated?  

What data from your deployments is being used for training? These are questions that should be assessed as part of your AI security programme, not assumed to be handled by the vendor. 

AI Attack Scenarios DigitalXRAID Has Executed in Real Engagements

The following scenarios are drawn from actual penetration tests conducted by DigitalXRAID’s security testing team against production and pre-production AI systems. Client details have been removed. The attack techniques, outcomes, and risks described are real. 

Scenario 1: Customer Database Exfiltration via a Retail Chatbot

A retail client deployed a customer service chatbot with read access to their customer database, enabling it to look up orders and provide account information. Within 15 minutes of beginning the engagement, DigitalXRAID’s testing team had extracted customer names, email addresses, phone numbers, and purchase histories through prompt injection. 

The chatbot had no meaningful authentication beyond assessing whether a request seemed reasonable. When the AI determined that helping with an account query was a reasonable request, it provided the data.  

The attack required no code, no credentials, and no technical knowledge. The AI thought it was helping. 

Scenario 2: Privilege Escalation in a PowerApps HR Tool

A low-code PowerApps application had been built by the HR team to answer employee questions about policies. The tool had integration with HR systems including salary data, performance reviews, and confidential employee records.  

By stating that administrative access was required to answer the query, the tester was granted access to all of it. No password cracking. No technical exploit. One sentence in a conversation. 

This scenario illustrates the specific risk of low-code AI applications: they’re built quickly by non-technical teams, often with broad system access, and with no security review. They’re frequently invisible to the security team entirely. 

Scenario 3: Phishing Email Sent via a Legitimate Business Domain

A customer service chatbot had the ability to send confirmation emails to customers. DigitalXRAID’s testing team convinced the AI to send a phishing email containing a malicious link to every customer in the database, by framing the request as a routine system test. 

The email arrived from the organisation’s own legitimate domain, sent through their own legitimate email infrastructure. The AI had no mechanism to distinguish a malicious instruction from a genuine operational request. The email looked exactly like legitimate company communications because it was sent from exactly the same systems. 

Scenario 4: System Prompt Extraction Revealing API Keys and Security Controls

Through targeted prompt engineering, the testing team extracted the complete system prompt from an AI deployment. The prompt revealed the data access controls in place, the thresholds that triggered manager approval or refusal, API endpoints, database schemas, and in one instance, hardcoded API keys embedded directly in the prompt. 

With the system prompt extracted, every protective control the organisation had implemented was visible. The team then used this information to bypass those controls systematically.  

The attack surface had expanded from the AI system itself to the entire backend architecture it described. 

Every scenario described here was executed against real systems. The entry barrier in each case was a conversation. 

The Hidden Risk: Low-Code and No-Code AI Applications

One of the fastest-growing and least-tested AI attack surfaces in enterprise environments is the low-code and no-code AI application. Tools like PowerApps, Make.com, n8n, and Microsoft Copilot Studio make it straightforward for employees across any department to build AI-powered workflows without involving IT or security teams.  

The result is a growing population of AI systems with access to SharePoint, databases, and APIs, built with zero security controls, and largely invisible to the security function. 

These aren’t niche or unusual deployments. According to Microsoft’s own telemetry, over 80% of the Fortune 500 is already deploying active AI agents built with low-code and no-code tools.  

DigitalXRAID’s Director of Security Testing has observed multiple internal tools built in PowerApps with direct access to SharePoint, databases, and external APIs, with no gateway, no access controls, and no logging. In some cases, these systems weren’t recorded in any IT asset inventory. 

The risk profile of these tools is identical to that of enterprise-built AI systems, and in some cases higher, because the absence of developer oversight means basic security hygiene is rarely applied. Session isolation, least-privilege access, input validation, and output controls are simply absent. 

If your organisation has deployed, or is considering deploying, AI through low-code tools, these systems need to be included in your AI security testing scope. They also need to be governed. For guidance on establishing that governance, see our guide to building an AI governance framework. 

What Does an LLM & GenAI Penetration Test Involve?

An effective LLM penetration test follows a structured but flexible five-phase approach, designed to reflect real-world attack scenarios while being tailored to how your specific AI systems are built and deployed. 

Phase 1: Discovery

Discovery is where every engagement begins, and it consistently reveals more than organisations expect. Scoping involves identifying which models are in use, how they’re deployed, what data they can access, and which systems they interact with.  

In practice, this phase regularly uncovers shadow AI deployments that IT didn’t know existed, undocumented integrations with third-party LLM APIs, and RAG systems pulling from sensitive data sources that weren’t scoped as part of the original assessment. You can’t test what you don’t know about. 

Phase 2: Threat Modelling

Threat modelling is where the testing team works with your team to understand what matters most. A chatbot leaking customer data carries different implications than an AI agent approving fraudulent transactions.  

Risk profiling shapes what gets tested and in what order, and allows for bespoke assessment scenarios tailored to your specific AI deployment and business context. 

Phase 3: Active Testing

This phase simulates real attacks against your AI systems. The testing team will attempt to extract system prompts, manipulate the AI into unauthorised actions, exploit plugins and integrations, test whether data is properly isolated between users, and assess whether the AI can access more data than it should.  

The key questions: can one user access another user’s data through the AI? Can the AI reach backend systems it shouldn’t be able to reach? 

Phase 4: Impact Assessment and Reporting

Every finding includes a detailed demonstration. Not abstract vulnerability descriptions, but the exact prompts used, what was achieved, the business risk created, and clear reproduction steps so your development team knows precisely what needs to be fixed.  

Evidence-based reporting means findings can be replicated and verified, and remediation can be prioritised based on actual business impact. 

Phase 5: Remediation Guidance and Retesting

Remediation guidance is specific to AI systems, not generic security advice. Input validation strategies, output sanitisation techniques, architectural recommendations, and access control changes are provided in the context of how your system is built.  

Once fixes have been implemented, retesting validates that they’re effective in practice. 

How Often Should AI Systems Be Penetration Tested?

AI security testing should be treated as an ongoing programme, not a one-off exercise. AI systems evolve continuously through model updates, new integrations, expanded capabilities, and changes to underlying data sources. Each change creates new potential vulnerabilities. 

DigitalXRAID recommends the following testing cadence based on system type and risk profile: 

• Quarterly assessments for high-risk, customer-facing AI systems with access to sensitive data or transaction capabilities 
• Every six months for customer-facing systems with lower development velocity 
• Bi-annual or annual testing for internal AI tools, adjusted based on how rapidly those tools are evolving 
• After every significant model update, new integration, or expansion of AI capabilities 
• Before deployment for new AI systems entering production 

If you already have a regular penetration testing programme, adding AI security testing is typically two to three additional days of engagement. If your organisation has already deployed AI systems without prior testing, that isn’t a barrier to starting. Existing production systems can be assessed, and the second-best time to test is now. 

Building Secure AI by Design: Preventative Controls

Testing identifies vulnerabilities in deployed systems. Secure-by-design controls reduce the attack surface before deployment.  

The following four control pillars should be validated for every AI system your organisation builds or deploys. 

Input Validation

AI systems require prompt injection detection and filtering that goes beyond traditional input validation. Traditional validation looks for malformed or malicious data. AI input validation looks for attempts to override the model’s instructions or manipulate its behaviour.  

The system prompt should be separated from user input. Rate limiting and anomaly monitoring should be implemented to detect unusual patterns. Tools such as Microsoft AI Foundry provide logging and monitoring capabilities that can support this. 

Access Control

Apply least-privilege principles to every AI system and agent. Does your chatbot genuinely need write access to your database? Should your AI assistant have access to all of SharePoint, or only the libraries relevant to its function?  

Session isolation must ensure that one user cannot access another user’s data through the AI. AI agents should be granted only the permissions they need to perform their defined function, no more. 

Output Handling

Treat AI responses as untrusted input to downstream systems. Just because your AI generated something doesn’t mean it’s safe to act on or pass to other systems. Sanitise outputs before using them in other applications.  

Implement Data Loss Prevention (DLP) capabilities to catch sensitive data in AI responses. Log all AI interactions for security analysis and anomaly detection. 

Architecture

Review AI integrations at the design stage, not after deployment. Implement centralised governance for low-code applications so that IT and security teams have visibility into what AI systems exist.  

Create secure templates that business users can build from. Maintain an AI inventory, you can’t secure, test, or govern what you don’t know exists.

How LLM & GenAI Penetration Testing Aligns with UK Compliance Expectations

AI security is increasingly intertwined with regulatory and governance requirements. Testing is no longer just a security best practice; it’s becoming an evidence requirement. 

AI Security and ISO 27001 Risk Management

AI systems introduce new information security risks that must be considered as part of an ISO 27001 risk assessment. LLM penetration testing provides evidence that AI-specific risks have been identified, assessed, and treated appropriately under your ISMS. 

Data Protection and Compliance Considerations

Where AI systems process personal data, risks such as data leakage and automated decision-making become particularly significant. AI systems testing helps identify scenarios where personal data could be exposed or misused, supporting compliance with GDPR and UK Data Protection obligations. 

Preparing for Evolving AI Regulation

AI regulation is evolving rapidly. Standards such as ISO 42001 and the EU AI Act reflect the growing expectation that organisations manage AI risks proactively. Embedding security testing into your AI governance framework demonstrates due diligence and supports long-term compliance as requirements mature. 

Who Should Consider LLM & GenAI Penetration Testing?

LLM penetration testing is relevant across a wide range of organisations and maturity levels. 

Organisations Already Using AI in Production

If AI systems are already influencing your customers, employees, or decisions, testing helps ensure that your risks are understood and controlled before an incident occurs. The incidents described in this article happened to organisations that hadn’t prioritised testing. The risks are immediate. 

Organisations Planning AI Adoption

Testing before deployment reduces the likelihood of security incidents and costly rework later. It’s significantly cheaper to identify and fix vulnerabilities before go-live than to remediate after an incident. 

Regulated and Data-Sensitive Sectors

Public sector bodies, financial services organisations, healthcare providers, and defence suppliers face heightened scrutiny and benefit most from proactive AI security assurance.  

The combination of sensitive data, regulatory obligations, and reputational risk makes these sectors particularly exposed to the consequences of ungoverned AI deployment. 

The Business Benefits of LLM & GenAI Penetration Testing

Beyond technical risk reduction, LLM penetration testing delivers clear business value.

Reducing Cyber and Regulatory Risk

Identifying weaknesses early prevents incidents that could lead to data breaches, regulatory action, or operational disruption. Given the Air Canada legal precedent, the regulatory dimension of AI security has real financial consequences. 

Protecting Brand Trust and Decision Integrity

AI outputs increasingly influence business decisions. Ensuring those outputs can’t be manipulated, and that your AI systems don’t create the next viral incident, protects your reputation and the integrity of AI-assisted decisions. 

Enabling Safe and Confident AI Innovation

Security testing enables innovation by providing the confidence that risks are understood and managed rather than avoided. Organisations that understand their AI security posture can adopt AI more confidently and move faster. 

Why Specialist Expertise Matters for AI Security Testing

AI security testing isn’t an extension of traditional penetration testing. It requires a different methodology, a different attacker mindset, and expertise in how LLMs actually behave.

Why Automated Tools Are Not Enough

Automated scanners struggle to assess AI behaviour, context, and intent. They can’t reliably identify prompt-based attacks or subtle manipulation techniques.  

Automated tooling has a role in coverage but can’t replace the judgement, creativity, and attacker mindset that human-led AI testing requires. 

The Importance of Human-Led AI Testing

Experienced testers bring the ability to adapt their approach in real time based on how a system responds. This is essential when testing systems designed to behave flexibly.  

The most impactful vulnerabilities in AI systems are often found through creative, iterative exploration, not scripted test cases. 

CREST and the Emerging AI Security Certification Landscape

CREST is actively developing a dedicated certification specifically for AI security testing. DigitalXRAID continues to make ongoing investment in AI security capabilities, with specialist testers able to uncover vulnerabilities in your systems and help you to protect your business from the attack vectors outlined above.  

Our testing team has completed specialist training in AI security methodology and brings direct experience from live client engagements across multiple AI deployment types. 

How DigitalXRAID Supports Secure AI Adoption

Specialist expertise is critical when navigating the evolving AI security landscape.

AI-Specific Penetration Testing Expertise

DigitalXRAID delivers penetration testing focused specifically on real-world AI deployments.  

Our five-phase methodology covers discovery, threat modelling, active testing, impact assessment, and remediation guidance, and is designed to integrate with your existing security testing programme rather than replace it. 

Practical, Business-Focused Reporting

Findings are presented in a way that supports decision-making, prioritisation, and remediation. You’ll receive the exact prompts used, evidence of what was achieved, business risk context, and clear reproduction steps.  

Learn more about DigitalXRAID’s OrbitalX Security Portal for tracking and managing security findings. 

Compliance Expertise

DigitalXRAID can provide guidance aligned with established and emerging standards, supporting the achievement and maintenance of ISO 27001 and ISO 42001 certification as AI governance expectations evolve. 

Visibility of AI Usage in Your Organisation

DigitalXRAID’s Security Operations Centre (SOC) service team uses advanced tooling, including Microsoft’s security suite, to provide visibility into how AI and LLM tools are being used across your organisation.  

This helps to identify when sensitive data may be entered into AI systems and supports proactive risk management alongside your testing programme. 

Long Term Security Partnership

AI security will continue to evolve as models develop, new attack techniques emerge, and regulatory requirements mature. Ongoing support from experts dedicated to cyber security ensures that your controls, testing, and monitoring remain aligned with the threat landscape.

Getting Started with LLM & GenAI Penetration Testing

Starting with the right preparation makes testing more effective and valuable.

What to Prepare Before Engaging a Testing Provider

Clear documentation, a well-defined scope, and agreed objectives help ensure meaningful results. The more your provider understands about how your AI systems are built, what data they access, and what they’re authorised to do, the more targeted and effective the testing will be.

When to Test: Before or After Deployment

Testing before deployment is always preferable and typically less complex. But if your organisation has already deployed AI systems without prior testing, that isn’t a barrier.  

Existing production and pre-production systems can be assessed. Every week without testing is a week of unquantified risk. 

Speak to DigitalXRAID About LLM & GenAI Penetration Testing

If you’re deploying, or planning to deploy, AI systems, now is the time to understand your risks and engage an LLM & GenAI penetration testing partner.  

To discuss your AI environment, security concerns, and testing requirements, get in touch with the DigitalXRAID team to get expert advice and learn how we’ve helped other organisations secure their AI systems. 

Frequently Asked Questions: LLM & GenAI Penetration Testing

What is LLM penetration testing?

LLM penetration testing assesses AI systems by attempting to manipulate model behaviour, prompts, and integrations in the same way a real attacker would. It covers prompt injection, data leakage, agent misuse, RAG poisoning, system prompt extraction, and integration vulnerabilities that traditional application testing doesn’t address. 

What is the OWASP LLM Top 10?

The OWASP LLM Top 10 is the industry-standard framework for AI security risk, defining ten critical vulnerability categories specific to large language model applications. It covers prompt injection (LLM01), insecure output handling (LLM02), training data poisoning (LLM03), model DoS (LLM04), supply chain vulnerabilities (LLM05), sensitive information disclosure (LLM06), insecure plugin design (LLM07), excessive agency (LLM08), overreliance (LLM09), and model theft (LLM10). It’s the foundation of how responsible AI penetration testing is structured. 

Is LLM penetration testing the same as application penetration testing?

No. LLM penetration testing focuses on AI-specific risks such as prompt injection, model behaviour manipulation, and agent misuse that traditional application testing doesn’t cover. It requires a different methodology, different tooling, and specialist expertise in how language models respond to adversarial input. 

Why does generative AI need penetration testing?

Generative AI introduces new attack paths and unpredictable behaviour that standard security testing can’t identify. The real-world incidents covered in this article show that AI systems can be exploited by anyone, using nothing more than a chat interface. Testing identifies and quantifies these risks before they result in data leakage, legal liability, or reputational damage. 

Do low-code AI tools like PowerApps need security testing?

Yes. Low-code and no-code AI applications carry the same security risks as enterprise-built AI systems, and in many cases higher risk because they’re built without security review, have broad system access, and are frequently invisible to IT and security teams. DigitalXRAID has executed successful attacks against PowerApps tools that exposed salary data, performance reviews, and confidential HR documents through a single conversation. 

What is AI red teaming and how does it differ from AI penetration testing?

AI red teaming and AI penetration testing are closely related disciplines. Penetration testing follows a structured methodology against defined systems and scope, typically producing a formal report of vulnerabilities and remediation guidance. Red teaming is more adversarial and open-ended, simulating a sophisticated attacker attempting to achieve a specific objective (for example, extracting customer data or causing a system to send malicious communications). Both are valuable, and DigitalXRAID can deliver both depending on your assessment objectives. 

How often should AI systems be penetration tested?

Quarterly for high-risk, customer-facing AI systems with access to sensitive data. Every six months for lower-risk customer-facing systems. Bi-annually or annually for internal tools, depending on how rapidly they’re evolving. Always after significant model updates, new integrations, or capability expansions. And always before deployment for new systems entering production. 

What are the common vulnerabilities in AI and LLM systems?

Common issues include prompt injection, sensitive data leakage and system prompt disclosure, excessive agent permissions, training data and RAG poisoning, insecure plugin design, and supply chain risks from third-party models. The OWASP LLM Top 10 provides a comprehensive categorisation of these risks. 

Can penetration testing prevent AI hallucinations?

Testing can’t eliminate hallucinations entirely, but it can identify scenarios where hallucinations create security or business risk, for example where the AI provides incorrect information that creates legal liability, as in the Air Canada case. 

What’s the cost of AI/LLM penetration testing in the UK?

Costs vary depending on scope, complexity, and integration depth. If you already have a regular penetration testing programme, adding AI security testing typically requires two to three additional days of engagement. For new or standalone AI security assessments, a scoping conversation will determine the appropriate investment based on the number of systems, integrations, and testing objectives. 

Does AI penetration testing impact live systems?

Testing can be conducted safely with agreed controls in place to minimise disruption to live environments. DigitalXRAID typically works with pre-production or staging environments where possible for active exploitation phases, with live system assessments scoped carefully to avoid operational impact.