[AI Penetration Testing]
We specialise in adversarial security testing of AI and ML systems, LLM prompt injection, RAG pipeline attacks, model extraction, agentic AI exploitation, and AI supply chain security. Purpose-built for organisations building or deploying AI.
OWASP
LLM Top 10 aligned testing methodology
Agentic
Coverage of AI agents, RAG, and tool-use systems
Novel
Attack research beyond standard checklists
Actionable
Developer-ready remediation guidance
Attack coverage
[01]
Direct and indirect prompt injection attacks against large language models. Jailbreak attempts, system prompt extraction, and instruction override testing. Covers both hosted APIs (OpenAI, Anthropic, Gemini) and self-hosted models.
[02]
Security testing of retrieval-augmented generation systems, data poisoning, context manipulation, unauthorised data retrieval, and cross-tenant data leakage in multi-tenant RAG architectures.
[03]
Attempts to extract model weights, training data, or proprietary system prompts through repeated querying. Membership inference attacks to determine whether specific data was used in training.
[04]
Security testing of AI agents with tool access, code execution, web browsing, file system access, and API calls. Privilege escalation through agent chains, unintended action execution, and sandbox escape.
[05]
Assessment of model provenance, fine-tuning pipeline security, and third-party model risks. Backdoor detection in fine-tuned models and evaluation of model cards and documentation for security gaps.
[06]
Full security review of applications built on AI, covering the traditional application attack surface (auth, injection, data exposure) plus AI-specific risks including output handling, trust boundaries, and human-in-the-loop bypass.
Methodology
[01]
Map the AI system architecture, models, APIs, data pipelines, agent chains, tool integrations, and trust boundaries. Identify the highest-risk attack surfaces.
[02]
AI-specific threat modelling covering adversarial inputs, data poisoning, model extraction, and agentic attack paths. Prioritised by business impact.
[03]
Manual adversarial testing by engineers with deep AI security expertise. Novel attack chains, not just standard prompt injection checklists.
[04]
Technical findings with developer-ready remediation guidance. Executive summary covering AI-specific risk posture and recommended controls.
Tooling & frameworks
LLM Testing
Adversarial ML
Model Analysis
Frameworks
RAG & Agents
Traditional AppSec
Why TechSonar
Our testers have deep expertise in both traditional application security and AI/ML systems, not generalists with a prompt injection checklist.
We conduct original research into AI attack techniques. Our engagements go beyond published checklists to find the vulnerabilities others miss.
We test the AI layer and the application layer. Most AI pentests ignore the traditional attack surface, we cover both.
Reports aligned to OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, and EU AI Act requirements.
[Get started]
Talk to us about your AI system, whether it is an LLM-powered product, an agentic workflow, or a RAG pipeline. We will scope an engagement that gives you real confidence in its security.