AI / LLM
Security Testing
Adversarial testing of AI-powered products and large language model deployments against the OWASP LLM Top 10 — prompt injection, jailbreaking, data leakage, and agent exploitation.
AI and LLM-powered products introduce a fundamentally new class of vulnerabilities. Prompt injection, insecure plugin integration, training data leakage, and jailbreaking can undermine application security controls in ways that traditional penetration testing methodologies do not cover.
We assess AI-powered applications against the OWASP LLM Top 10, combining automated adversarial testing with manual operator-driven attack scenarios. This includes agentic systems, RAG pipelines, LLM-integrated APIs, fine-tuned models, and customer-facing chatbots.
LLM security is an emerging and rapidly evolving field. Our practitioners actively follow LLM security research, contribute to the community, and maintain up-to-date knowledge of attack techniques as they develop — ensuring your assessment reflects the current state of the art.
Architecture Review & Attack Surface Mapping
Understanding the AI system's architecture — model provider, system prompt design, embedding databases, plugin integrations, agent chains, and data flows — to identify the full attack surface before testing begins.
Prompt Injection Testing
Direct and indirect prompt injection attacks — attempting to override system instructions, manipulate model behaviour, and cause the LLM to act outside its intended scope. Both user-facing inputs and data ingestion pipelines (indirect injection) are tested.
Jailbreaking & Safety Bypass
Adversarial prompting techniques including role-play, persona switching, token manipulation, and multi-turn attacks to bypass content moderation, safety filters, and ethical guardrails — testing the robustness of alignment controls.
Data Leakage & System Prompt Extraction
Attempts to extract system prompts, training data, personally identifiable information, or other sensitive data from the model or its connected data stores through adversarial querying, context reconstruction, and membership inference techniques.
Plugin & Tool Integration Testing
Security assessment of all tools and APIs accessible to the LLM — testing for injection via tool outputs, over-privileged tool access, confused deputy attacks, and the potential for injected prompts to cause the agent to perform malicious actions on connected systems.
RAG Pipeline Security
Assessment of Retrieval-Augmented Generation pipelines for document poisoning, embedding store manipulation, retrieval manipulation, and indirect prompt injection via malicious documents ingested into the knowledge base.
Reporting & Mitigation Guidance
Findings mapped to OWASP LLM Top 10 controls, with specific prompt hardening recommendations, input/output validation guidance, and architectural recommendations for reducing LLM attack surface.
LLM security is a specialist discipline that requires deep understanding of model behaviour, not just application security. Our operators actively research LLM attack techniques and stay current with the rapidly evolving threat landscape — you won't receive an assessment built on last year's knowledge.
We test your AI systems the way real adversaries will — with creativity, persistence, and an understanding of both the technology and the business impact. Our reports provide concrete, implementable guidance rather than generic observations.