

AI & LLM SecurityTesting Services
AI systems break in ways traditional apps don't. Prompt injection, data leakage through model outputs, jailbreaks that bypass safety guardrails. We test LLMs, RAG pipelines, and AI-powered applications for the vulnerabilities that standard pentesting won't catch.

LLM & Model Expertise
We test GPT, Claude, Gemini, open-source models, and custom fine-tuned LLMs
OWASP LLM Top 10
Full coverage of prompt injection, data poisoning, system prompt leakage, and all Top 10 risks
Adversarial Red Teaming
We attack your AI the way real threat actors would, jailbreaks, extraction, and manipulation
The AI Security Landscape
A web app pentest won't find a prompt injection vulnerability. A network scan won't detect that your chatbot leaks customer data when asked the right question. AI systems have their own class of vulnerabilities. From jailbreaks that bypass safety filters to adversarial inputs that corrupt model behaviour and they need testing built specifically for how they fail.
Organisations deploying ChatGPT-style applications, custom LLMs, RAG systems, AI agents, and machine learning models face risks including unauthorised data extraction, model theft, adversarial inputs, and unintended harmful outputs. These vulnerabilities can lead to data breaches, compliance violations, financial losses, and severe reputation damage.
When AI is making decisions that affect customers, finances, or operations, a security flaw isn't just a bug, it's a liability.
Our AI Security Testing Services
LLM Application Security
Your LLM-powered app is only as secure as the prompts it processes. We test applications built on GPT, Claude, Gemini, and open-source models for prompt injection, jailbreaking, and data leakage. The attacks that scanners and traditional pentests miss entirely.
Output Safety & Guardrail Testing
Safety filters and content guardrails are only effective until someone finds the right bypass. We test your model's output controls for harmful content generation, filter evasion techniques, and edge cases where safety alignment breaks down.
RAG & Vector Database Security
RAG pipelines add a whole new attack surface. We test your retrieval layer. Pinecone, Weaviate, Chroma, pgvector, or custom setups for unauthorised knowledge access, embedding poisoning, and indirect prompt injection through retrieved documents.
Model Supply Chain Security
The model you downloaded from Hugging Face might not be what you think it is. We assess model provenance, check for poisoned pre-trained weights, test for malicious payloads in model files, and review your ML pipeline dependencies for known vulnerabilities.
OWASP LLM Top 10 Coverage
Industry Applications
Finance & Banking
Fraud detection models, credit scoring systems, and trading algorithms that attackers can manipulate through adversarial inputs, plus chatbots that may leak customer financial data through prompt injection
Healthcare
Diagnostic AI and treatment recommendation systems where adversarial attacks could cause misdiagnosis, and where patient data extraction violates HIPAA
Enterprise SaaS
Code assistants that may leak proprietary source code, knowledge bases that expose confidential documents, and AI tools with access to internal systems
E-commerce
Recommendation engines vulnerable to poisoning, pricing models susceptible to manipulation, and customer-facing chatbots that can be tricked into revealing backend data
AI Regulations Are Here
The EU AI Act is now in effect. NIST has published the AI Risk Management Framework. ISO 42001 sets the standard for AI governance. If your AI systems haven't been security tested, you're not just carrying risk, you may be falling short of regulatory requirements. Prompt injection, data leakage, and output manipulation aren't theoretical risks anymore. They're audit findings.
AI is moving fast. Security testing for it should move just as fast. Let's make sure your models aren't leaking data or doing things they shouldn't.
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