AI security testing for LLM applications, agents, MCP servers, and RAG pipelines, with manual attack validation, clear fixes, and retest evidence.
AI Security Testing covers the AI systems you already run: the model, the application built around it, and every tool, data source, and identity the model can reach. Five focused services sit under this core. The application penetration test probes the model-facing layer and the interfaces, sessions, and access controls around it. The agentic assessment examines how an autonomous system plans, remembers, and acts on your behalf, and what it can do without a human in the loop. The connector assessment tests the components that hand the model new tools and the credentials behind them. The data boundary test checks whether retrieval ever returns information a user is not entitled to see. The red team exercise runs objective-driven campaigns against your guardrails. Every engagement follows one rhythm: scope in writing, test by hand under agreed spend and rate caps with a pause contact, report with reproduction steps and fixes, then retest with evidence.
For CTOs, CISOs, and product owners who have shipped, or are about to ship, an assistant, copilot, agent, or retrieval feature, whether it runs on a hosted model service or a model you operate yourself. Engage before launch, before a customer security review, or after a change widens what the model is allowed to reach.
We start from a walkthrough with the people who built the system: the model behind it, the application around it, the tools and data it can reach, and the identities it acts with. We agree which focused services apply, the roles to test as, staging or production, the spend and rate limits, and a named contact who can pause the work.
The same principal who scoped the work runs it. We attempt the attacks that matter by hand: overriding the model's instructions with hidden input, tricking it into misusing its tools, pulling data a user should not see, and abusing the access controls around it. Because model output varies, every attempt is logged and successes are counted. Scanners support the work but are never the deliverable.
You receive findings with reproduction transcripts, a severity rating based on impact and how easily each issue is exploited, and a fix aimed at the layer that actually closes it. An executive summary is written for leadership and customer reviews. After you remediate, we retest each finding and package the evidence for customers, insurers, and auditors.
LLM penetration testing for prompt injection, data disclosure, unsafe outputs, and application access controls, with reproducible findings and fixes.
From $6,000
Scope, pricing & FAQsAgentic AI security assessments test tool access, memory, approval controls, and autonomous actions to identify misuse and limit potential damage.
From $8,400
Scope, pricing & FAQsMCP server security testing for tool misuse, excessive permissions, and credential handling across the AI connectors you build, host, or approve.
From $3,600
Scope, pricing & FAQsRAG security testing checks document access, retrieval poisoning, and permission changes, with evidence of where an AI assistant can expose data.
From $6,000
Scope, pricing & FAQsAI red teaming tests prompt injection, guardrail bypasses, and multi-turn manipulation against agreed objectives, with documented results and fixes.
From $9,000
Scope, pricing & FAQsThe selected services, deliverables, access requirements, and any follow-up validation are agreed in your engagement scope.
A focused conversation. A clear scope. A practical path to stronger security.
