LLM Security Testing

We Test Your AI Before Attackers Exploit It

Large Language Models introduce a new class of vulnerabilities – prompt injection, data exfiltration, jailbreaks, and model manipulation. We’re one of the few UK firms with dedicated LLM security testing capabilities, combining deep offensive security experience with practical AI knowledge to find vulnerabilities that traditional security tools can’t detect.

Whether you’re building a customer-facing chatbot, an internal knowledge assistant, or an AI-powered workflow, we test the full attack surface: prompt injection chains, training data extraction, output manipulation, and integration-level vulnerabilities.

What We Test

Our methodology is aligned with the OWASP Top 10 for LLM Applications and covers the full spectrum of AI-specific attack vectors:

• Direct Prompt Injection: We attempt to override system prompts, extract hidden instructions, bypass content filters, and manipulate the model into performing unintended actions. We test escalating techniques – from simple instruction overrides to multi-turn conversation manipulation and encoding-based bypasses (Base64, ROT13, Unicode tricks).

• Indirect Prompt Injection: If your LLM processes external data (emails, documents, web pages, database content), we test whether an attacker can embed malicious instructions in that data to hijack the model’s behaviour. This is the most dangerous and underestimated LLM vulnerability – we test it across every data source your model ingests.

• RAG Poisoning & Data Source Manipulation: For Retrieval-Augmented Generation systems, we test whether we can manipulate the knowledge base, vector database, or document store to influence model outputs. We look for injection points in document ingestion pipelines, metadata manipulation, and embedding collision attacks.

• Tool Use & Agent Exploitation: If your LLM has access to tools (APIs, databases, code execution, file systems), we test whether prompt injection can trick the model into calling tools with attacker-controlled parameters – executing arbitrary API calls, exfiltrating data, or modifying backend systems.

• Training Data & System Prompt Extraction: We attempt to extract the system prompt, fine-tuning data, and any confidential information embedded in the model’s context window. This includes testing for memorisation of PII from training data and extraction of proprietary business logic encoded in system instructions.

• Output Manipulation & Hallucination Exploitation: We test whether the model can be manipulated into generating harmful, misleading, or brand-damaging content. We also assess whether hallucinated outputs could lead to security-relevant consequences – such as fabricated legal advice, incorrect financial figures, or fake support instructions.

• Excessive Agency & Permission Boundaries: We assess whether the LLM has more permissions than necessary. Can it access data across tenants? Can it perform actions that should require human approval? We map the blast radius of a successful prompt injection against the model’s actual permissions.

Our Testing Methodology

• Architecture Review: We map your LLM implementation end-to-end – the model provider (OpenAI, Anthropic, self-hosted), orchestration layer (LangChain, LlamaIndex, custom), RAG pipeline, tool integrations, guardrails, and output filters. We identify trust boundaries and data flows to understand where attacks can enter and what damage they can cause.

• Prompt Injection Testing: We use a structured library of injection techniques – instruction override, context manipulation, persona switching, payload encoding, multi-turn escalation, and delimiter confusion – testing each against your specific guardrails and system prompt design.

• Integration Security Testing: We test the traditional application security of the surrounding infrastructure – API authentication, session management, input validation, rate limiting, and access controls on the endpoints that expose your LLM. Many LLM vulnerabilities are amplified by weak application-level security.

• Guardrail Evasion: If you’ve implemented content filters, output sanitisation, or prompt firewalls, we systematically test bypass techniques. We assess whether your guardrails are effective against novel attack patterns, not just known prompt injection strings.

What We Typically Find

Representative findings from recent LLM assessments (anonymised):

• Indirect Prompt Injection via Customer Emails: A support chatbot processed incoming customer emails and used an LLM to draft responses. We embedded hidden instructions in an email body that caused the model to include the contents of the system prompt – including internal routing rules and escalation criteria – in its reply to the “customer.”

• Tool Use Exploitation for Data Exfiltration: An internal AI assistant had access to a database query tool. Through multi-turn prompt injection, we convinced the model to execute a query that returned all employee records including salary data, then format the results as a “summary report” displayed in the chat – bypassing the intended access controls.

• RAG Poisoning via Document Upload: A knowledge-base chatbot allowed users to upload reference documents. We uploaded a document containing hidden prompt injection instructions in white-on-white text. When other users asked questions that triggered retrieval of this document, the model followed our injected instructions instead of its system prompt.

• Cross-Tenant Data Leakage: A multi-tenant SaaS product used a shared LLM with per-tenant system prompts. By manipulating the conversation context, we extracted data from another tenant’s knowledge base that had been cached in the model’s context window from a previous session.

Why FORTBRIDGE for LLM Security?

• Offensive Security + AI Expertise: Our consultants combine traditional penetration testing skills with hands-on experience building and attacking LLM applications. We understand both the model-level risks and the application-security fundamentals that amplify them.

• OWASP LLM Top 10 Aligned: Our methodology covers all categories in the OWASP Top 10 for LLM Applications, providing a recognised benchmark for your AI security posture.

• Full-Stack Assessment: We don’t just test the model in isolation. We assess the entire application – from the user interface and API layer through the orchestration framework and tool integrations to the model itself.

• Practical Remediation: Every finding comes with specific, implementable recommendations – from system prompt hardening and input sanitisation to architectural changes that reduce the blast radius of prompt injection attacks.

Get Started Today

Don’t leave your AI systems vulnerable to attack. Partner with FORTBRIDGE for expert pentesting services tailored to Large Language Models. Safeguard your business, protect sensitive data, and ensure your AI operates securely in an evolving threat landscape.

Book a Discovery Call to discuss your requirements and get a scoping estimate.