AI & LLM Agent Security Testing
AI agents now act with real permissions, call real tools and read untrusted data — a powerful new attack surface most security programs don't cover. We specialize in testing LLM-powered and agentic systems end to end, from prompt injection to full tool-chain and supply-chain compromise.
AI Agent Penetration Testing
Penetration testing for autonomous AI agents — tool-use abuse, goal hijacking, privilege escalation and sandbox escape across agentic LLM systems.
./open →Prompt Injection Testing
Prompt injection testing — direct and indirect injection across every untrusted input path, including RAG and tool outputs, with data-exfiltration proof.
./open →LLM Jailbreak & Guardrail Testing
LLM jailbreak and guardrail testing — systematic evaluation of safety controls, policy evasion and harmful-output elicitation with reproducible bypasses.
./open →RAG Pipeline Security Assessment
RAG security assessment — vector-store poisoning, context leakage, access-control gaps and retrieval-based prompt injection across your RAG pipeline.
./open →MCP Server & Tool-Chain Security Testing
MCP server security testing — tool schema tampering, confused-deputy paths, credential-scope leakage and abuse of Model Context Protocol integrations.
./open →AI Supply Chain Security Audit
AI supply chain security audit — model provenance, plugin and extension risk, dataset integrity and fine-tune leakage across your AI/ML dependencies.
./open →LLM Application Penetration Testing
LLM application penetration testing — the full stack around your model: prompts, plugins, APIs, output handling and the OWASP Top 10 for…
./open →Agentic AI Threat Modeling
Agentic AI threat modeling — structured analysis of autonomous agent workflows, trust boundaries and abuse cases to secure AI systems by design.
./open →What AI and LLM security testing covers
AI agent penetration testing attacks autonomous, tool-using systems for goal hijacking, tool abuse and sandbox escape. Prompt injection testing probes every untrusted input path, including indirect injection through documents and tool output. Jailbreak and guardrail testing evaluates your safety controls against current bypass techniques. RAG pipeline security assessment covers poisoning, leakage and access-control gaps. MCP server security testing treats Model Context Protocol integrations as privileged attack surface. AI supply-chain audits review models, datasets and plugins. LLM application penetration testing covers the whole stack, and agentic AI threat modeling secures systems by design before you build.
Why AI agent security matters
Unlike a chatbot that only returns text, an AI agent takes real actions — it browses, runs code, moves data and calls APIs with its own permissions. The moment such an agent ingests attacker-controlled text, prompt injection becomes a control channel, turning a helpful assistant into an insider threat. Prompt injection is ranked the number-one risk in the OWASP LLM Top 10, and indirect injection through retrieved content is the variant most teams miss. As AI moves from demos into production — often handling sensitive data and privileged actions — testing this new attack surface is no longer optional; it is a baseline requirement for responsible deployment.
How to choose the right service
If you are shipping an autonomous or tool-using agent, start with AI agent penetration testing and prompt injection testing. If your product retrieves from a knowledge base, add a RAG pipeline security assessment. Teams exposing tools to agents via the Model Context Protocol need MCP server security testing. For public-facing generative features, jailbreak and guardrail testing protects your brand and compliance posture. And if you are still designing the system, agentic AI threat modeling removes whole classes of risk before you write code. We help you match the service to where your AI system is in its lifecycle.
Frequently asked questions
What makes AI agents harder to secure than normal apps?
Do you test agents built on OpenAI, Anthropic and open-source models?
Which AI security frameworks do you align to?
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