Chatbot Security Assessment

It evaluates a conversational AI system for security weaknesses reached through the conversation — prompt injection and jailbreak, data leakage, guardrail bypass, session flaws, and the security of the backend actions it can trigger — following the OWASP Top 10 for LLM Applications and the OWASP AI Testing Guide, with each finding proven and rated.

Jailbreak is getting the model to ignore its own safety rules — to say what it was told not to say. Prompt injection is getting it to follow an attacker's instruction over the application's, often through content it reads rather than a direct request. They overlap, and a chatbot assessment tests both.

Many safety filters are tuned for English. Persian character variants (ی/ي, ک/ك), zero-width characters, mixed script, and Finglish can change what the filter sees without changing what the model understands, letting a payload through. An assessment that only tests English misses these, so we test the Persian-specific evasions directly.

Yes — through its system prompt, its knowledge base, or the history of another user's session if state is not properly isolated. We test whether the chatbot can be led to reveal its instructions, retrieve documents the user has no right to, or carry information across sessions.

We test the boundary: where the chatbot can trigger a backend action, we check that the action verifies who is asking and stays within their permissions. A full assessment of the backend systems themselves is a separate scope we can add, but the chatbot-to-backend gate is part of this service.

Conversational testing is largely safe, but tests that could trigger a real action or reach real customer data are agreed in advance and run carefully. We prefer a staging instance that mirrors production, and when we must test the live system we set limits and keep a contact on your side to pause at once.

Each finding gets a CVSS score for a common baseline, adjusted by the real impact — what it leaks or triggers, how easily, and whether it crosses a user boundary — and is mapped to the OWASP Top 10 for LLM Applications. A guardrail slip that only affects tone is not the same as one that reaches customer data.

Duration depends on the chatbot's complexity — its knowledge base, its backend actions, and the languages it serves. A typical assessment runs about one to three weeks, and we give a firm estimate after scoping. Re-testing after you remediate is included.