LLM Security Testing
LLM Application Penetration Testing
Large language models are powerful – and vulnerable to specific attacks. We test your LLM-based applications systematically against all relevant security risks.
LLM applications introduce new, specific security risks that cannot be fully addressed with traditional pentesting methods. We test your LLM-based applications systematically following OWASP LLM Top 10 and additional recognised frameworks.
Prompt Injection
Prompt injection is the most dangerous vulnerability in LLM applications (OWASP LLM01). Attackers inject malicious instructions into LLM inputs to manipulate the system, bypass security mechanisms, or trigger unintended actions. We test both direct and indirect prompt injection – including injection through documents, web pages, and database content retrieved via RAG.
OWASP LLM Top 10
RAG and Agentic Systems
Retrieval-Augmented Generation (RAG) and autonomous agents introduce specific risks including data source poisoning, tool call manipulation, and agent hijacking. We test these complex system architectures with specialised methods tailored to multi-step agentic workflows and autonomous tool execution.
Application, Integration, and Model Layers
An LLM application penetration test goes beyond testing the language model in isolation. LLM applications consist of multiple layers, each with its own attack surface. The application layer covers classic web application security: authentication and authorisation, session management, input validation, API security, and data flows between frontend, backend, and the LLM endpoint. Vulnerabilities here – such as missing access controls or exposed API keys – are often more easily exploitable than LLM-specific attacks.
The integration layer connects the application to the LLM model, external tools, vector databases, and business systems. Risks arise from insecure tool calls, overprivileged system access by the LLM agent, and unprotected prompt contexts that expose confidential system instructions. In agent-based architectures with autonomous tool calling (function calling, code execution), this layer is critical: a compromised agent can execute database queries, send emails, or call external APIs.
Our approach combines classical penetration testing methodology (OWASP WSTG, Burp Suite Pro for API testing) with LLM-specific attack techniques (prompt injection, jailbreaks, indirect injection via external data sources). The result is a complete security picture of your LLM application – not just the model, but the entire attack chain from user input to backend integration.
LLM Testing Portfolio
- Prompt injection (direct & indirect)
- Jailbreak testing
- Data extraction tests
- RAG poisoning tests
- Tool / plugin security
- Excessive agency tests
- Model inversion
- Red teaming for LLMs
Frequently Asked Questions
What is the difference between an LLM pentest and an LLM application penetration test?
A pure LLM pentest focuses on the language model itself – prompt injection, jailbreaks, model extraction. An LLM application penetration test covers the entire application: the application layer (auth, APIs, session management), integration layer (tool calls, database access, external APIs), and model layer together. In practice, the most critical vulnerabilities are often found in the application or integration layer, not in the model itself.
Which LLM applications can be tested?
We test all application architectures that use LLMs: chat applications, RAG systems (Retrieval-Augmented Generation), autonomous agents with tool calling, internal copilots, and API services exposing LLM endpoints. Supported models and platforms: OpenAI GPT-4/o, Anthropic Claude, Google Gemini, self-hosted open-source models (Llama, Mistral), and enterprise LLM deployments (Azure OpenAI, AWS Bedrock).
Which frameworks does Blackfort use for LLM application security testing?
We combine OWASP LLM Top 10 for LLM-specific vulnerabilities, OWASP WSTG (Web Security Testing Guide) for the application layer, MITRE ATLAS (Adversarial Threat Landscape for AI Systems), and proprietary test methodology for agentic AI architectures. For API testing we use Burp Suite Professional; for LLM-specific attacks, specialised test frameworks.
How does an LLM application penetration test with Blackfort work?
The test begins with a scoping phase: which system components and user scenarios are in scope? Which roles and access levels exist? We combine authenticated and unauthenticated tests, examine all API endpoints, and conduct targeted LLM-specific attacks. The output is a structured report with classified findings, proof-of-concept evidence, and concrete remediation recommendations.
Is an LLM application penetration test relevant for regulated organisations?
Yes – increasingly so. The EU AI Act classifies many AI applications in regulated sectors (financial services, healthcare, critical infrastructure) as high-risk. High-risk AI systems must undergo security testing before deployment. DORA requires financial entities to systematically test ICT risks including AI-based systems. An LLM application penetration test provides auditable evidence for both regulatory frameworks.
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Secure Your LLM Applications
We identify vulnerabilities in your LLM systems before attackers do – with a methodology that covers the full application stack, not just the model.