Introduction
AI agents and large language models (LLMs) are becoming part of business processes — answering customers, processing documents, controlling internal systems. This brings a new category of security risks that most organizations are not prepared for.
These are not science fiction scenarios. These are real, documented vulnerabilities that can be exploited today.
What Makes AI Systems Different?
Traditional software does exactly what the code tells it. An AI model interprets natural language — and can therefore interpret instructions that don’t belong there.
Key differences from traditional software:
- Input = instruction. In a regular application, input is data. In an LLM, input is text that the model processes as a command. An attacker doesn’t need to find a buffer overflow — a sentence is enough.
- Non-deterministic behavior. The same input can lead to different outputs. Testing AI system security is exponentially harder than testing an API.
- Context as attack vector. Everything the model sees — documents, emails, web pages, database results — can contain instructions the model will follow.
- Tools as impact multiplier. If an AI agent has access to email, CRM, database, or APIs, a successful attack doesn’t just have informational impact — it has operational impact.
Who Is at Risk?
Any organization that:
- Runs a chatbot with access to internal data
- Uses AI agents to automate tasks (emails, tickets, orders)
- Connects LLMs to internal systems via APIs, plugins, or connectors
- Lets AI process documents, emails, or web content from third parties
- Uses RAG (Retrieval-Augmented Generation) over its own knowledge base
What Are the Risks?
| Risk Category | Example | Potential Impact |
|---|---|---|
| Prompt injection | Attacker embeds instructions in text that AI processes | AI changes behavior, reveals system instructions, bypasses rules |
| Data leakage | AI reveals internal information, PII, trade secrets in response | Compliance violation, financial loss, reputational damage |
| Tool misuse | AI agent performs unauthorized action (sends email, deletes record) | Direct damage to systems and data |
| Error chaining | Error in one automation step propagates to others | Cascading failure, difficult root cause analysis |
| Hallucination abuse | AI fabricates facts and presents them as true | Wrong decisions, legal risk |
Why Act Now?
- Regulation is coming. The EU AI Act categorizes AI systems by risk and requires security measures.
- Attacks are trivial. Prompt injection requires zero technical skills — just text input.
- Damages are real. Data leaks, unauthorized actions, compromised workflows — these are not theoretical threats.
- Post-deployment fixes cost more. Security by design costs a fraction of incident response after a breach.
What’s Next?
In the following sections, we cover:
- What is prompt injection — how it works, what attacks look like, why it’s dangerous
- AI agent risks — what happens when AI has too many permissions
- How to defend — specific technical and procedural measures
- Test it yourself — send your AI agent to our test page and find out if it’s vulnerable