Key Takeaways
- AI systems require specialist security testing beyond traditional penetration testing.
- The OWASP Top 10 for LLMs helps organisations identify the most critical AI security risks.
- Prompt injection, data leakage, excessive permissions, and supply chain vulnerabilities are among the biggest threats.
- Agentic AI, MCP servers, and orchestration frameworks introduce new attack vectors that require dedicated assessment.
- LLM security testing helps organisations deploy AI safely, securely, and with confidence.
On this page
- What is LLM and Chatbot Security Testing?
- Why Does AI Need Its Own Security Testing Framework?
- What is the OWASP Top 10 for LLMs?
- The Most Significant Risks in the OWASP LLM Top 10
- Does the OWASP Top 10 Cover Everything?
- Who Needs LLM and Chatbot Security Testing?
- How is LLM Testing Different from Penetration Testing?
- What Does an OmniCyber LLM Security Assessment Involve?
- Is Your Organisation Ready for LLM Security Testing?
Is Your AI Safe to Deploy? What Security Leaders Need to Know About LLM and Chatbot Penetration Testing
Your organisation is deploying AI. Whether that means a customer-facing chatbot, an internal productivity tool, or a large language model integrated into your core systems, the attack surface has changed. The security testing approach needs to change with it.
LLM and chatbot penetration testing is a specialist discipline, distinct from traditional application security testing, and grounded in a dedicated vulnerability framework: the OWASP Top 10 for Large Language Model Applications. Here is what security leaders need to understand about the risks, the testing process, and what good looks like.
What Is LLM Security and Chatbot Security Testing?
LLM and chatbot security testing is a structured assessment of the security risks specific to AI-powered systems. It examines:
- How the system responds to adversarial inputs.
- Whether it can be manipulated into disclosing sensitive information or behaving in unintended ways.
- Whether the controls around it are sufficient to manage the risks it introduces.
Unlike standard penetration testing, which focuses on conventional vulnerabilities like authentication weaknesses or misconfigured infrastructure, LLM penetration testing requires specialist knowledge of how language models work and how they can be exploited.
Why Does AI Need Its Own Security Testing Framework?
Traditional security testing was designed for deterministic systems: software that behaves the same way every time. Language models are non-deterministic: their behaviour can be influenced by how a prompt is constructed, the context provided, and the data they have access to. That creates a category of vulnerability that did not exist before, where an attacker can:
- Craft inputs designed to manipulate the model’s behaviour
- Extract information the model should not share
- Cause the model to take actions it should not take
The OWASP Top 10 for LLMs is the most widely adopted, although not exhaustive, framework for identifying and addressing these risks.
What Is the OWASP Top 10 for LLMs and Why Does It Matter?
The OWASP Top 10 for Large Language Model Applications identifies the ten most critical security risks for systems built on LLMs. It matters because it gives organisations and security testers a consistent, evidence-based reference point for what to test and what good remediation looks like. For security leaders it is the equivalent of the OWASP Top 10 for web applications.
It covers the most commonly observed risks across the industry, but there are vulnerabilities beyond the Top 10 and it should not be treated as a checklist.
What Are the Most Significant Risks the OWASP LLM Top 10 Covers?
The ten risks in the 2025 framework cover the full lifecycle of an LLM application. Here’s what they mean for your organisation:
Prompt Injection: An attacker crafts inputs that cause the model to ignore its instructions, potentially bypassing safety controls or extracting confidential system information.
Sensitive Information Disclosure: The model reveals data it should not, including personal data, intellectual property, or internal business logic from its training data or system prompt.
Supply Chain Vulnerabilities: Weaknesses in third-party models, datasets, plugins, or APIs your system relies on can introduce risk regardless of how well your own implementation is secured.
Data and Model Poisoning: Tampered training or fine-tuning data can compromise model behaviour in ways that are difficult to detect, particularly relevant for organisations using custom models.
Improper Output Handling: Model output passed to downstream systems without sufficient validation can create injection vulnerabilities in those systems, with the LLM acting as the attack vector.
Excessive Agency: A model given more capability to take actions than its function requires becomes a significant risk if manipulated, whether that means sending emails, executing code, or accessing systems.
System Prompt Leakage: System-level instructions can be extracted through carefully crafted prompts, exposing business logic, security controls, or configuration details to an attacker.
Vector and Embedding Weaknesses: Vector databases used to give models access to external information can be exploited to manipulate what the model retrieves and includes in its responses.
Misinformation: Models generate plausible but incorrect responses, creating risk wherever output informs decisions, communications, or actions without sufficient human oversight.
Unbounded Consumption: Models can be induced to consume excessive computational resources, creating denial of service conditions or significant unexpected costs.
Does the OWASP Top 10 Cover Everything I Need to Worry About?
The OWASP LLM Top 10 is the right foundation for any assessment, but the AI landscape is developing rapidly and new technologies are expanding the attack surface in ways the framework does not always address. Areas that require attention beyond the OWASP Top 10 include:
- Agentic workflows: AI systems that can plan and act autonomously introduce risks around task execution, privilege escalation, and unintended side effects beyond the scope of standard LLM testing.
- Model Context Protocol (MCP) servers: An emerging standard for connecting AI models to external tools and data sources, MCP servers represent a growing class of integration risk that requires specific assessment.
- LLM harnesses and orchestration frameworks: Tools that chain multiple models or agents together create complex trust boundaries and data flows that introduce vulnerabilities at the orchestration layer.
Who Needs LLM and Chatbot Security Testing?
Any organisation that has deployed, is deploying, or is evaluating the deployment of an AI system that uses a large language model. That includes:
- Customer-facing chatbots and virtual assistants
- Internal AI tools for productivity, HR, legal, or finance functions
- AI-powered search and knowledge management systems
- Agentic AI systems that can take actions on behalf of users
- Applications built on third-party LLM APIs such as OpenAI, Anthropic, or Google
Using a third-party AI product rather than building your own does not remove the risk. It changes its shape.
How Is LLM Testing Different From a Standard Penetration Test?
A standard penetration test focuses on the technical infrastructure around an application. That includes:
- Authentication and authorisation controls
- Input validation
- Network configuration and exposed services
Those tests remain necessary for AI systems, but they do not address the risks specific to the model itself.
LLM application penetration testing examines a different set of questions:
- How does the model respond to adversarial prompts?
- Can its guardrails be bypassed?
- What information can it be induced to disclose?
- Are the trust boundaries between the model and the systems around it appropriately defined and enforced?
What Does an LLM Security Assessment From OmniCyber Involve?
Our adversarial LLM testing and chatbot security assessments are conducted by specialist red team consultants using the OWASP Top 10 LLM 2025 as a foundation. Large language model security testing goes deeper based on the specific architecture, use case, and risk profile of your deployment and covers:
- Prompt injection and jailbreak testing
- System prompt extraction attempts
- Sensitive data disclosure assessment
- Excessive agency and privilege evaluation
- Output handling and downstream injection testing
- Supply chain and integration risk review
- Agentic workflow and orchestration testing where relevant
- MCP server and tool integration assessment where applicable
Findings are delivered in a format that is actionable for both technical and leadership audiences, and typically include:
- A prioritised list of identified vulnerabilities mapped to the OWASP LLM Top 10
- Plain-language explanations of business risk for each finding
- Remediation guidance for development and security teams
- Re-testing support to verify identified issues have been resolved
How Do I Know if My Organisation Is Ready for LLM Security Testing?
Testing is often most valuable at the point where an AI system is moving from development or pilot into production, or where an existing deployment has not previously been assessed.
The questions worth asking before you start are:
- Do we have a clear understanding of what data the model has access to?
- Do we know what actions the model can take on behalf of users?
- Have we defined the boundaries of what the model should and should not do?
- Do we have visibility of the third-party components and APIs the system relies on?
If the answers are incomplete, that is not a reason to delay testing. It is a reason to prioritise it.
OmniCyber Security provides specialist LLM and chatbot penetration testing as part of our Red Teaming and application security services. If your organisation is deploying AI and wants to understand the risks before they materialise, get in touch to discuss how we can help.
Jennifer Goulbourne
Jennifer is a Digital Marketing Executive at OmniCyber Security, where she's responsible for engaging the company's existing customer base across digital channels and creating helpful resources on threat intelligence and security operations for cybersecurity professionals and business leaders.
