15+ AI Products & LLM Applications targets assessed

AI product security assessments
for prompt flows, agent actions, APIs, and user-data boundaries

AI products mix prompts, model APIs, retrieval systems, user uploads, admin tooling, and downstream actions. The risk is not only classic web flaws, but also unsafe tool execution, prompt abuse, and weak trust boundaries around generated output.

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What we test in AI Products & LLM Applications

Authorization flaws around prompts, conversations, and uploaded data

Agent or tool-execution abuse paths

Prompt and retrieval flow manipulation with downstream impact

API and token handling weaknesses in model integrations

Support/admin tooling privilege escalation

Unsafe workflow chaining between user input and sensitive actions

AI Products & LLM Applications-specific concerns

Prompt abuse with real business impact

User-data and conversation isolation

Tool and action execution boundaries

Fast-moving product changes around model integrations

How a AI Products & LLM Applications engagement runs

AI product testing covers both halves of the stack. The classic web layer — authentication, tenant isolation on conversations and uploads, admin tooling — is tested like any SaaS. On top of it we probe the model-facing layer: prompt and retrieval flows that can be steered into leaking other users' context, tool-execution paths where generated output reaches a sensitive action without review, and API tokens embedded in the chain with more scope than they need.

Findings distinguish between 'the model said something odd' and 'the model did something harmful' — only the second kind gets reported, with the full chain from input to impact documented so your team can reproduce it.

Regulatory and compliance context

AI products are entering a real regulatory perimeter: the EU AI Act now imposes obligations on high-risk systems, GDPR governs personal data in prompts and retrieval stores, and enterprise procurement has settled on SOC 2 plus model-governance questionnaires as the baseline ask. A cross-user context leak or an agent executing an unauthorized action is a breach event with notification duties — not a model-quality issue. Findings are reported with the trust boundary and the data class affected, which is what compliance teams and auditors need.

What we typically find in AI Products & LLM Applications

Most AI-product findings are conventional web flaws with higher stakes: IDORs on conversation and document endpoints, API tokens for model providers exposed in client bundles, and retrieval pipelines that answer from another tenant's documents. The AI-specific layer contributes tool-execution paths where a prompt steers an agent into an action the user never authorized, and system-prompt or context leakage that reveals credentials or other users' data. We see both on nearly every engagement — the second category is what makes an AI product different to secure.

What the report includes

Every verified finding with a reproducible proof-of-concept: the exact request sequence, the role it ran under, and the data or action it reached.

Severity rated on impact in your sector — funds moved, records exposed, tenants crossed — not a generic CVSS guess.

Remediation guidance per finding, written for the team that will implement the fix, plus a re-test window to verify each patch.

An executive summary your leadership and security questionnaire reviewers can read without a security background.

AI Products & LLM Applications assessment FAQ

Do you test prompt injection and agent behavior?

Yes, where it produces real impact — data exfiltration, unauthorized tool calls, cross-user context leaks. Cosmetic jailbreaks without consequences are noted but not treated as findings.

How do you scope a fast-changing AI product?

Scope is set around the trust boundaries — auth, data isolation, tool execution — rather than specific features, so the assessment stays meaningful as the product ships.