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Glossary

Chatbot Security: Operating AI Assistants Safely

Chatbot security for businesses: how to detect and contain prompt injection, data leakage and excessive permissions in LLM-based AI assistants.

Definition: Chatbot security covers the technical and organizational steps that protect AI assistants built on large language models (LLMs). They guard against misuse, manipulation and data leaks by mistake. This applies to public chatbots for customers. It also applies to internal assistants that can read company files, email or business apps.

Why chatbots need their own security approach

A classic rule-based chatbot follows fixed decision trees. An LLM-based assistant reads free text. It cannot reliably tell the operator's instructions apart from what the user types. Both end up as text in the same context window. Normal web apps simply do not have this kind of attack surface.

Modern assistants also rarely work alone. They may:

  • search internal knowledge bases via retrieval-augmented generation (RAG)
  • read tickets and mailboxes
  • trigger actions through APIs, such as placing an order, booking a meeting or creating a record

The more a chatbot may do, the more harm it can cause once someone has tricked it.

Typical attack and failure scenarios

The OWASP Top 10 for LLM Applications (2025 edition) is a good reference. For company chatbots, these items matter most:

  • Direct prompt injection: A user types phrases like "ignore all previous instructions". The goal is to get past the guardrails, create banned content or get the bot to make promises the company never meant to make.
  • Indirect prompt injection: The harmful instruction is not in the user's message. It is hidden in a document, web page or email that the bot reads. This is a big risk for bots that sum up inboxes or fetch web pages.
  • Sensitive information disclosure: An internal bot shows content the employee should not see. Often the RAG index was built without the original access rights.
  • System prompt leakage: Attackers trick the bot into showing its system instructions. This is a real problem when login data, internal URLs or business logic are stored there.
  • Excessive agency: The chatbot has write rights or tool access it does not need. Once tricked, it can trigger real actions.
  • Improper output handling: The bot's answers go straight into web pages, database queries or scripts without checks. The result can be classic flaws such as cross-site scripting.
  • Unbounded consumption: Automated mass requests drive up API costs or make the service unusable.

Relevance for mid-sized companies

Many mid-sized firms use chatbots in customer service or as internal knowledge tools. They often build them on cloud services from large providers. The real risk is rarely the language model itself. It lies in how the bot is connected:

  • which data gets indexed
  • which rights the bot gets
  • how its answers are reused

Firms should also expect to be held to what their chatbot says. In 2024, a Canadian tribunal ordered an airline to pay a refund its chatbot had wrongly promised.

Practical safeguards

  1. Define purpose and data scope: Write down what the bot is for, which data sources it may use and which topics it should turn down.
  2. Enforce user rights: Internal bots should only fetch files with the rights of the person asking. A central index without rights checks is a common design flaw.
  3. Minimal tool rights: Prefer read-only access. Ask a human to confirm any write action or action that moves money.
  4. No secrets in the system prompt: API keys and login data belong in backend code, not in the model's instructions.
  5. Filter inputs and outputs: Use input filters against known injection patterns. Use output filters against personal data. Encode all output before it reaches HTML or queries.
  6. Logging and monitoring: Log chats in line with data protection rules. Look for odd patterns and spot abuse attempts. Set rate limits and budget caps.
  7. Test on purpose: Run red teaming or a penetration test with LLM-specific test cases. Do this before go-live and after major changes.
  8. Involve staff: Users of internal bots should know which data they may enter. They should also know that answers can be wrong.

Regulatory context

Article 50 of the EU AI Act (Regulation (EU) 2024/1689) applies since 2 August 2026. People must generally be told that they are talking to an AI system, unless this is obvious.

If the chatbot handles personal data, GDPR rules apply. Key points are purpose limitation, data minimization and security of processing under Article 32. Cloud providers need a data processing agreement.

Some firms fall under NIS2. In Germany, NIS2 is put into law through the amended BSI Act (BSIG). For these firms, a live chatbot also belongs in regular cyber risk management.

Conclusion

A safe chatbot depends less on the "right" model and more on sound design: limited rights, checked data sources and controlled output. SecTepe supports risk analysis. We also test AI assistants for LLM-specific flaws.

Frequently asked questions

Can prompt injection be prevented completely?

Not with today's technology. Filters lower the risk. The key is to limit the possible harm with minimal rights and human approval steps.

Is an internal chatbot less risky than a public one?

Not always. Internal bots often have access to far more sensitive data. So weak rights checks can do more harm there than in a public FAQ bot.

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