AI & LLM Pentest

An AI & LLM Pentest is a security assessment for applications that use artificial intelligence or large language models at their core.

It is important to make the distinction clear: this is not a pentest performed by an AI agent. It is a human-led security test of AI-powered applications, such as:

• AI/LLM-based customer support chatbots
• Internal copilots using LLMs
• RAG-based search and knowledge systems
• AI agents with connected tools
• Applications using model APIs

The goal is to identify vulnerabilities that could compromise the confidentiality, integrity, or availability of the application.

No. The pentest itself is not executed by an AI agent.

WebSec performs a human-led security assessment focused on applications that use AI, LLMs, AI/LLM-based chatbots, AI agents, RAG pipelines, copilots, or model APIs. The testing may include specialized tooling, but the assessment, exploitation, validation, and reporting are performed by security experts.

This ensures findings are accurate, contextual, and relevant to your business risks.

AI and LLM application pentesting is recommended for organizations that build, deploy, or integrate AI-powered systems into business-critical processes. This includes:

• SaaS providers using AI/LLM-based chatbots or copilots
• Financial institutions using AI agents or decision-support tools
• Healthcare institutions using AI-powered assistants or triage systems
• Government and regulated organizations using RAG pipelines or model APIs

Any organization using generative AI features should meet strict information security requirements, including periodic pentesting.

Organizations using AI-powered applications should align with the OWASP Top 10 for LLM Applications, which outlines security risks such as:

• Prompt injection
• Sensitive information disclosure
• Supply chain risks
• Improper output handling

A key part of these requirements is the AI & LLM pentest, which ensures applications with AI or LLMs at their core resist real-world model-driven cyber threats.

The scope depends on the application, but testing commonly includes:

1. Application logic - Authentication, authorization, business logic, and exposed functionality.
2. Prompt handling - System prompts, user prompts, indirect prompts, and prompt boundaries.
3. Model integrations - Model APIs, orchestration layers, plugins, tools, and agent workflows.
4. Data flows - RAG pipelines, vector databases, documents, embeddings, logs, and memory.
5. Output handling - How model responses are validated, encoded, rendered, or passed downstream.

The assessment focuses on the full application around the model, not only the model itself.

The pentest is a core component of an AI application assessment.
While the broader assessment checks governance, policies, processes, and technical safeguards, the pentest focuses specifically on:

• Detecting exploitable AI and application vulnerabilities
• Validating the effectiveness of implemented guardrails
• Testing whether connected tools and data sources can be abused

This ensures both administrative readiness and technical resilience.

The main differences are:

• AI application focus - Testing targets applications that use LLMs, RAG pipelines, AI agents, tools, and model APIs.
• Specific scope - Includes prompts, outputs, retrieval, data flows, model behavior, and connected tools.
• Risk mapping - Findings are linked to AI-specific categories such as the OWASP Top 10 for LLM Applications.
• Unique testing - Includes prompt injection, indirect prompt injection, excessive agency, model abuse, and data leakage testing.

A regular pentest can be broader in scope and may not fully cover these AI-specific attack paths.

An AI & LLM Pentest is important to:

• Ensure secure handling of personal and sensitive data.
• Protect against prompt injection and AI agent abuse.
• Validate that AI-generated output cannot harm downstream systems.
• Verify that connected tools, APIs, RAG pipelines, and data sources are properly restricted.

Without this testing, organizations risk data leakage, unauthorized tool use, model abuse, reputational damage, and operational disruption.

In most cases:

• Annually - As part of the yearly security assessment.
• After major changes - For example, new models, new tools, RAG pipeline changes, new data sources, or significant code changes.

Testing frequency ensures security controls remain effective against evolving threats.

• AI & LLM Pentest - Technical testing to identify exploitable vulnerabilities in an application with AI or LLMs at its core.
• AI Assessment - Broader audit including governance, policy review, process evaluation, and control verification.

The pentest is a subset of the assessment, providing evidence that technical defenses meet modern security standards.

Preparation tips:

1. Review the OWASP Top 10 for LLM Applications to understand the requirements.
2. Identify all AI-related systems in scope.
3. Document prompts, models, tools, APIs, data sources, and RAG flows before the test.
4. Ensure proper logging and monitoring are in place to detect test activities.
5. Assign a technical contact to assist during the test.

Good preparation helps maximize the value of the pentest and minimizes disruption.

If critical vulnerabilities are found:

• They must be remediated before the assessment can be completed.
• A retest is typically required to confirm fixes.
• Persistent failure may result in delayed launches or increased business risk.

Fast remediation and retesting ensure minimal impact on operations.

There is no single universal “LLM pentester certification.”
However, this type of pentest should be performed by an experienced security company or team that:

• Has proven penetration testing expertise.
• Understands the OWASP Top 10 for LLM Applications.
• Understands how AI/LLM-based chatbots, AI agents, RAG pipelines, prompts, and model APIs are built.
• Can produce reports that align with modern security assessment requirements.

Choosing a provider with both technical skills and knowledge of model-specific risks ensures the results will be useful as part of your assessment.

An AI & LLM Pentest report includes:

• Executive summary
• List of identified vulnerabilities
• Proof of exploitation (where applicable)
• Risk ratings and impact analysis
• Remediation recommendations
• Mapping of findings to OWASP LLM requirements

The report serves both technical teams and compliance stakeholders.

The duration depends on scope complexity:

• Small AI integrations - 3-5 days
• Large or complex applications with AI or LLMs at their core - 1-2 weeks

Additional time may be needed for retesting after remediation.

WebSec offers:

• Experienced security testers with AI application expertise.
• Human-led testing of applications that use LLMs, AI/LLM-based chatbots, AI agents, RAG pipelines, and model APIs.
• Proven methodology aligned with the OWASP Top 10 for LLM Applications.
• Comprehensive reports ready for stakeholder review.
• Ongoing support for remediation and retesting.

This ensures a smooth and secure assessment process for your AI-powered application.