AI Security
AI/ML System Security Assessment
Comprehensive security evaluation of AI and machine learning systems — from LLM prompt injection to model extraction.
What is AI Security?
As organisations integrate AI and machine learning into their products and operations, they introduce a new class of attack surface that traditional security testing cannot address. Our AI Security service is purpose-built to evaluate the security of AI/ML systems — covering LLM-based applications, classical ML pipelines, and the infrastructure they run on.
We test for the OWASP LLM Top 10 vulnerability classes: prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, and more. Beyond the Top 10, we evaluate adversarial robustness, model inversion attacks, membership inference, and extraction attacks against your deployed models.
Our assessors hold specialist AI security certifications (CAISP, CARTP) and have hands-on experience attacking production LLM deployments, RAG pipelines, and ML inference APIs — not just academic familiarity with the concepts.
Why it matters
- Prompt injection is the SQL injection of the AI era — attackers can hijack LLM behaviour through crafted user input, bypassing all application-layer controls
- LLMs integrated into business workflows can be manipulated to exfiltrate data, execute unauthorised actions, or produce harmful outputs at scale
- Model extraction attacks allow competitors to replicate your proprietary AI investment using a fraction of the compute cost
- Training data poisoning can degrade model performance or introduce backdoors that only activate on specific inputs — undetectable by standard monitoring
- AI supply chain risk (via third-party model weights, fine-tuning datasets, or inference APIs) is poorly understood and frequently unmitigated
Our methodology
1. AI System Architecture Review
We map your complete AI system — LLM provider, embedding model, vector database, RAG pipeline, tool integrations, system prompts, and output handling — to understand the full attack surface before active testing begins.
2. Prompt Injection & Jailbreak Testing
Systematic testing of direct and indirect prompt injection across all user-controlled input surfaces. We test cross-context injection (via documents, web content, external APIs fed to the LLM) and multi-turn manipulation sequences.
3. Adversarial & Model-Level Testing
Testing for model extraction (black-box query attacks), membership inference, adversarial input sensitivity, and training data leakage. Where applicable, we test ML model APIs for evasion attacks against classification or detection models.
4. Infrastructure & Supply Chain Review
Review of LLM provider integrations, API key management, model registry security, fine-tuning pipeline security, and dependency risk in AI/ML toolchains (LangChain, LlamaIndex, Hugging Face, etc.).
Frequently asked questions
What LLM providers and frameworks do you test?
We have tested applications built on OpenAI (GPT-4, GPT-4o), Anthropic (Claude), Google (Gemini), and open-source models (Llama, Mistral). For frameworks, we cover LangChain, LlamaIndex, Semantic Kernel, and custom RAG implementations.
How is AI Security testing different from traditional application testing?
Traditional app testing operates on deterministic code. LLMs are probabilistic — the same input can produce different outputs, and vulnerabilities manifest through natural language manipulation rather than binary exploitation. The testing methodology, tooling, and operator skill set are fundamentally different.
Do you test the underlying AI infrastructure (GPU servers, model storage)?
Yes. AI infrastructure — model registries, fine-tuning pipelines, GPU clusters, and vector database deployments — is often configured with weaker security than application infrastructure. We include infrastructure review as part of a comprehensive AI security assessment.
Can you test AI agents and autonomous workflows?
Yes — agentic AI systems are a primary focus of our AI Security practice. Agents with tool access (web browsing, code execution, database access, email) create severe prompt injection risks. See also our AI Red Teaming service for full adversarial simulation of AI agents.
How do you test for training data poisoning?
We review your fine-tuning pipeline, dataset sources, data validation controls, and model behaviour for signs of poisoning. For production models, we probe for backdoor triggers and anomalous behaviour under targeted inputs. For future engagements, we also assist with designing poisoning-resistant training pipelines.
Deliverables
OWASP LLM Top 10 Assessment
Coverage report against all 10 OWASP LLM vulnerability categories
AI Attack Surface Map
Annotated diagram of your AI system architecture with identified risk areas
Prompt Injection Evidence Package
Working payloads, conversation traces, and exploitation proof-of-concepts
Model Risk Assessment
Assessment of extraction risk, inversion risk, and adversarial input sensitivity
Remediation Guidance
AI-specific controls: prompt hardening, output filtering, guardrail design, monitoring recommendations
Retest Verification
Post-remediation testing of confirmed injection and extraction vulnerabilities
Industries served
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All engagements are led by certified operators with unlimited retests until every critical finding is resolved.