Assessment

DEFINITION

What is LLM Assessment

A Large Language Model (LLM) Security Assessment is a targeted evaluation of the security risks associated with deploying and integrating LLMs—such as GPT, PaLM, or LLaMA—within applications or systems. It focuses on identifying vulnerabilities such as prompt injection, data leakage, insecure API interactions, and inadequate access controls. The objective is to ensure the safe and responsible use of LLMs by assessing input and output handling, authentication mechanisms, and potential abuse vectors, while also evaluating compliance with data protection and regulatory standards. This assessment is essential for organisations utilising LLMs to mitigate AI-specific threats and to safeguard the confidentiality, integrity, and availability of their systems and data.

benefits

Why should you do it

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Privacy Enhancement

Assessments help ensure that LLMs do not inadvertently disclose sensitive, personal, or confidential information. By safeguarding user data, they promote greater trust in AI-driven applications and responsible data handling.

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Compliance Assurance

By evaluating LLM deployments against applicable legal and regulatory requirements, such as the UK GDPR, assessments assist organisations in maintaining compliance. This reduces the risk of legal penalties and reputational damage linked to non-compliance.

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Ethical Alignment

Security assessments consider the ethical implications of LLM use, encouraging the responsible design and implementation of AI systems. This leads to outputs that better align with organisational values and societal norms.

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Transparency and Accountability

Assessments improve visibility into how LLMs handle prompts, process information, and generate responses. This supports clearer explanations of AI behaviour, which is particularly important in regulated sectors such as healthcare, finance, and public services.

methodology

Our approach

Attack Vector has developed a comprehensive methodology for evaluating Large Language Models (LLMs). To enhance our approach and ensure a thorough assessment, we leverage the OWASP Top 10 for LLMs. For an in-depth understanding of potential attacks on LLMs, please refer to our blog. We emphasise the importance of the scoping process in LLM assessments. Our methodology involves identifying all systems directly linked to your AI implementation and gaining a detailed understanding of their interactions. This enables us to tailor specific attack scenarios and test for data exfiltration vulnerabilities.

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Scope Definition

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Threat Modelling

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Vulnerability Assessment

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Authentication and Authorisation

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Data Exfiltration Testing

The assessment begins with a clear definition of its scope. This includes identifying which LLM(s) are in use, the applications and platforms they are embedded in, and the associated data sources and storage mechanisms. At this stage, the organisation’s goals and expectations for the review are also agreed upon, such as preventing data leakage, ensuring compliance, or evaluating exposure to specific attack vectors.

The threat modelling phase identifies potential attack scenarios based on the system architecture and usage patterns. External and internal threats are considered, including:

Malicious or manipulated inputs
Data poisoning during training or fine-tuning
Adversarial attacks designed to disrupt outputs
Data leakage risks from model responses
Authentication and access control vulnerabilities

This stage provides the foundation for a targeted testing strategy.

The LLM is systematically tested for security issues, focusing on how it handles inputs and generates outputs. This includes assessment against emerging threats such as the OWASP Top 10 for LLMs, which covers:

Prompt Injection
Insecure Output Handling
Training Data Poisoning
Model Denial of Service (where applicable)
Supply Chain Vulnerabilities
Sensitive Information Disclosure
Insecure Plugin Design
Excessive Agency
Overreliance on Model Outputs
Model Theft

The assessment involves the use of both manual and automated testing techniques, looking for real-world attack paths across the LLM’s implementation.

The review evaluates access controls and how users or systems authenticate when interacting with the LLM. This includes session management, token validity, user role enforcement, and whether authorisation checks are properly implemented to restrict access to sensitive features or data.

Specific attention is given to whether attackers could use the LLM to access and extract sensitive data. This includes prompt-based techniques to elicit private information, as well as indirect attacks through connected systems or APIs. Results are analysed to assess how easily an attacker could exploit such weaknesses.

FAQ

Further Information

Can testing be performed on third-party or API-based LLMs?

Yes. While internal LLMs allow for deeper testing, even third-party or hosted models (e.g., OpenAI, Anthropic) can be assessed through black-box techniques, such as crafted prompts, output analysis, and interaction flow reviews.

How is LLM testing different from traditional application testing?

Traditional testing focuses on software logic and system vulnerabilities, whereas LLM testing includes dynamic, behaviour-based evaluation of how the model processes and generates content in response to user input. LLMs behave probabilistically and can be influenced by prompt design or training data, requiring different testing strategies.

What standards or frameworks are used in LLM security testing?

LLM security testing leverages a combination of emerging security standards, regulatory requirements, and industry best practices designed to address the complex threat landscape associated with large language models (LLMs). The OWASP Top 10 for Large Language Models is a targeted framework developed by the Open Worldwide Application Security Project (OWASP) to address security risks specific to LLM-based applications. It serves as a critical reference in LLM security assessments.

What types of risks are assessed during LLM testing?

Common risk areas include:

Prompt injection and unsafe user inputs
Sensitive data exposure or leaks via model output
Insecure plugin or tool integrations
Misuse of APIs and third-party services
Authentication and authorisation flaws
Overreliance or excessive trust in model output
Bias, unfairness, or harmful content generation

Why is LLM security testing important?

LLMs are increasingly integrated into business-critical applications but can introduce unique risks such as prompt injection, biased outputs, model manipulation, or unauthorised data access. Security testing helps protect sensitive data, maintain compliance, and ensure trustworthy AI behaviour.