AI and LLM Penetration Testing

AI and LLM penetration testing is a controlled security assessment of applications that use large language models, generative AI, AI agents, RAG pipelines, plugins, tools, vector databases, and model-connected workflows. Penva tests how attackers could manipulate prompts, extract sensitive data, abuse tool access, bypass guardrails, poison retrieval content, trigger insecure outputs, or chain AI behaviour with traditional application and API vulnerabilities.

AI and LLM penetration testing in Australia typically starts from AUD $2,000 for a focused assessment. The final price depends on the number of AI workflows, model integrations, user roles, tools or agents, RAG sources, API endpoints, data sensitivity, and whether the system includes autonomous actions such as email, file access, code execution, payments, or customer support workflows. Penva provides a fixed-price quote after a free scoping call.

Most AI and LLM penetration tests take 5 to 10 business days of active testing, plus 1 to 2 days for reporting. A simple chatbot can often be assessed faster, while a complex AI agent with RAG, tool use, API integrations, user permissions, and sensitive data access may take 2 to 3 weeks. Penva shares findings progressively so engineering teams can begin guardrail and access-control fixes early.

It is increasingly expected when AI systems process sensitive data or influence business decisions. ISO 27001, SOC 2, privacy reviews, enterprise procurement, and AI governance programs commonly require evidence that high-risk systems are security tested. Penva provides audit-ready AI and LLM pentest reports covering prompt injection, sensitive data exposure, tool abuse, insecure outputs, access control, logging, and remediation validation.

AI red teaming often focuses on model behaviour, safety, jailbreaks, harmful outputs, and misuse scenarios. AI penetration testing focuses on exploitable security risk across the full application, including prompts, APIs, authentication, authorisation, tools, plugins, RAG content, vector databases, data flows, and backend systems. Penva combines AI-specific testing with traditional application security to identify real compromise paths, not just unusual model responses.

Penva uses AI carefully as an assistant, not as the tester. AI tools can help generate payload variations, map workflows, and improve coverage, but every finding is manually validated by a human penetration tester. This is essential for AI systems because impact depends on context, permissions, tool access, data exposure, business logic, and whether an attacker can turn model behaviour into a real security outcome.

Penva follows the OWASP Top 10 for Large Language Model Applications, OWASP API Security Top 10, OWASP Top 10, NIST SP 800-115, and practical AI red-team testing methodology. Our process covers scoping, AI workflow mapping, prompt and context analysis, RAG and data-source review, tool and agent permission testing, exploit validation, traditional API testing, reporting, and remediation retesting.

Penva tests AI and LLM systems in staging or controlled environments by default to reduce risk to production users, customer data, model costs, and business workflows. If production testing is required, we coordinate test accounts, approved prompts, rate limits, and safe execution boundaries. For AI agents with tool access, we confirm permissions and non-destructive testing rules before attempting exploit validation.

Yes. Every Penva fixed-price AI and LLM penetration test includes one free retest within 60 days of the original engagement. After remediation, we re-check prompt injection paths, access-control fixes, tool restrictions, data exposure controls, insecure output handling, and other reported issues. The updated report can be used for customer assurance, compliance, procurement, and internal AI governance evidence.

Yes. Penva's testers hold recognised penetration testing certifications including CREST, OSCP, and HTB CPTS, with experience across web applications, APIs, cloud, identity, and AI-enabled systems. This matters because serious AI security issues often require both AI-specific testing and traditional exploitation skills across APIs, access control, backend services, and cloud environments.

AI and LLM systems should be tested before launch, after major model or prompt changes, after adding new tools or agents, after connecting new data sources, and at least annually for compliance or customer assurance. Systems that process sensitive data, perform actions, or influence high-risk business decisions should be tested more frequently because AI behaviour, prompts, integrations, and retrieval content change over time.

Penva commonly finds prompt injection, indirect prompt injection through documents or web content, sensitive information disclosure, insecure output handling, excessive agency, weak tool permissions, insecure plugin or API integrations, RAG data leakage, vector database exposure, weak tenant isolation, model denial-of-service risks, guardrail bypasses, and traditional access-control flaws around AI workflows.

Penva supports SaaS platforms, FinTech, healthcare, education, e-commerce, professional services, AI startups, and government supply-chain vendors across Australia. We work with teams in Melbourne, Sydney, Brisbane, Perth, Adelaide, and Canberra that are building AI chatbots, AI agents, internal copilots, customer support assistants, RAG search systems, and AI-enabled business workflows.

Yes. Penva can sign a mutual NDA before any prompts, model architecture, RAG data sources, API documentation, credentials, customer workflows, or commercial information is shared. We can also work under your MSA, supplier agreement, data-handling requirements, or enterprise procurement process.