AI & LLM Penetration Testing
Your AI feature does not need to fail loudly to create risk. Canary Trap tests whether attackers can manipulate your AI, expose sensitive data, abuse connected tools, or turn trusted integrations against you.
What we test, review, and validate.
Hands-on, senior-led testing supported by tools and threat intelligence, never replaced by them.
Outcome of this engagement
AI risk often sits in the prompts, retrieval layer, connected tools, permissions, workflows, and data the model can reach. AI & LLM Penetration Testing helps your team validate whether AI-enabled features behave safely under adversarial use and whether the surrounding application, data, and integration layers create exploitable risk.
- Direct and indirect prompt injection
- System prompt extraction, override, and instruction bypass
- Output validation and guardrail bypass
- Sensitive data leakage from training, prompts, or retrieval
- Cross-tenant, cross-user, or unauthorized retrieval issues
- RAG source manipulation, poisoning, and source-of-truth abuse
- Tool / function-calling abuse and over-privilege
- SSRF, exfiltration, and integration pivoting
- Cost, rate-limit, and cost and resource-consumption attacks
AI security findings your team can act on.
An AI security test is only valuable if the findings help your team make safer product, engineering, and risk decisions.
Canary Trap reports are written to support remediation, leadership visibility, compliance conversations, and customer or auditor requests.
AI security should be validated before trust is assumed.
AI-enabled features can expand an application’s attack surface quickly, especially when they connect to private data, retrieval systems, tools, agents, or customer workflows.
This engagement gives your team a defensible view of how your AI features behave under adversarial pressure, including what was tested, what was validated, what creates real risk, and which controls or design changes should be prioritized next.
A transparent process from scope to retesting.
We adapt the steps to your scope and environment — you always know where we are and what comes next.
We confirm AI feature scope, model and provider context, prompts, retrieval sources, user roles, connected tools, environments, rules of engagement, timing, contacts, and communication process.
Our testers identify, investigate, and validate exploitable weaknesses across prompts, retrieval flows, authorization boundaries, agent actions, tool calls, integrations, and data exposure paths.
We document findings with evidence, severity, business context, reproducible adversarial test cases, and practical remediation guidance.
Your team addresses the findings with clear direction from the report and findings review.
We retest remediated findings within the defined window to validate that the risk has been addressed.
The proof behind this engagement.
AI & LLM Penetration Testing is not just about getting a chatbot to say something weird. The security risk usually comes from what the feature can access, retrieve, trigger, expose, or automate.
Canary Trap brings senior offensive security expertise, structured methodology, and practical reporting to help your team understand how AI-enabled systems behave under adversarial use.
Senior-led testing
Testing is led by experienced offensive security professionals, not handed off to junior scanner operators.
Application-aware AI testing
We test the AI feature in the context of the application, including roles, workflows, integrations, data access, retrieval behaviour, and connected tools.
Human-led validation
Tools support the process. They do not replace judgment. Our testers validate exploitability, investigate context, and look for realistic attack paths.
Practical reporting
Findings include the technical detail needed for remediation and the business context needed for leadership, compliance, and customer conversations.
Project management
Every engagement includes clear communication, defined expectations, and project management throughout the testing lifecycle.
Retesting and validation
Retesting helps confirm that remediated findings have actually been addressed, not just marked complete.
AI risk rarely exists in isolation
Most AI-enabled features sit inside a broader application, API, cloud, identity, and data ecosystem. These are common pairings with AI & LLM Penetration Testing.
AI & LLM penetration testing questions, answered plainly.
AI and LLM penetration testing evaluates how artificial intelligence and large language model-enabled applications behave under adversarial use. It tests prompt injection, sensitive data exposure, retrieval-augmented generation risk, agent and tool abuse, authorization gaps, integration risk, and unsafe system behaviour.
Canary Trap combines manual AI security testing, adversarial test cases, application-layer analysis, reporting, and retesting to help teams reduce AI-enabled security risk.
Canary Trap can test AI-enabled features such as chatbots, copilots, retrieval-augmented generation systems, agent frameworks, embedded LLM workflows, AI-assisted search, AI customer support tools, and AI features inside larger web or mobile applications.
Canary Trap tests your implementation, integration, application surface, prompts, retrieval flows, data access, agents, and connected tools. The underlying third-party model is only tested if it is contractually permitted and included in scope.
Not always. Many AI and LLM penetration testing engagements focus on production behaviour, prompts, retrieval systems, connected tools, authorization boundaries, and integration logic rather than model training data.
If training data, fine-tuning data, or retrieval sources are relevant to the risk model, they can be discussed during scoping.
Yes. Testing may include direct prompt injection, indirect prompt injection, system prompt extraction, instruction override, guardrail bypass, and prompt-based abuse of connected tools or data sources.
Yes. Canary Trap can test retrieval-augmented generation systems for sensitive data exposure, unauthorized retrieval, cross-user or cross-tenant data access, source manipulation, poisoning, and source-of-truth abuse.
Yes. Canary Trap can test agent frameworks, plugins, tool calls, function-calling workflows, permissions, and connected integrations for abuse paths, over-privilege, data exposure, SSRF, exfiltration, cost abuse, and unintended actions.
Yes. Findings can be mapped to the OWASP Top 10 for LLM Applications where applicable.
The mapping is useful for reporting and risk communication, but it should not be the full testing strategy. AI security risk often depends on application context, data access, permissions, retrieval behaviour, and integration design.
Most AI and LLM penetration testing engagements run one to three weeks of active testing, depending on feature complexity, data access, retrieval architecture, agent behaviour, number of roles, integrations, and testing objectives.
Cost depends on scope, including the AI feature surface, model use case, retrieval architecture, number of roles, integrations, connected tools, environments, and testing objectives.
Canary Trap prices from scope, not from a generic rate card.
Yes. AI and LLM penetration testing can support common compliance, customer assurance, and internal risk-management requirements. Canary Trap reports provide technical remediation detail while also supporting audit, leadership, and customer conversations.
Yes. Retesting of remediated findings is included within the defined engagement window after report delivery.
Model evaluation often focuses on model quality, safety, accuracy, or bias. AI and LLM penetration testing focuses on security risk across the AI-enabled application, including prompts, retrieval, authorization, agents, tools, integrations, data access, and abuse paths.
Scoping typically requires a description of the AI feature, model or provider context, prompts, retrieval sources, roles, connected tools, integrations, environments, test accounts, testing objectives, technical contacts, and rules of engagement.
A scoping call is used to confirm the right testing approach before work begins.
Canary Trap reviews the findings with your team, explains the most important risks, provides remediation guidance, and retests remediated findings within the defined window.
Ready to scope your AI & LLM Penetration Testing?
A short scoping call is enough to align on your AI feature, model use case, integrations, data access, timing, testing objectives, and the right next step.
Working against a launch, audit, renewal, or AI rollout deadline? Tell us the date and we’ll work backwards from it.
