Prompt Injection – The No. 1 Threat to Corporate Chatbots
Silent Sabotage of Your AI
Artificial intelligence is increasingly supporting enterprise processes – from customer service and data analysis to automating internal tasks. However, this also increases the number of new threats previously unknown in the IT world. One of them is prompt injection, an attack that is particularly dangerous for organizations using artificial intelligence, especially language-based models (LLMs).
Artificial intelligence is increasingly supporting enterprise processes – from customer service and data analysis to automating internal tasks. However, this also increases the number of new threats previously unknown in the IT world. One of them is prompt injection, an attack that is particularly dangerous for organizations using artificial intelligence, especially language-based models (LLMs).
Corporate chatbots and AI assistants improve communication and processes, but they can also… open the door to cyberattacks. One of the new attack techniques is prompt injection – an attack recognized by OWASP (Open Worldwide Application Security Project) as the most serious risk to solutions based on large language models (LLM01:2025). Using this technique, attackers can prompt AI to perform actions that its creators never intended. And the consequences may be more serious than they appear.
What Is Prompt Injection?
In a nutshell, a prompt injection attack involves injecting specially crafted commands (prompts) into AI input to induce the model to perform actions contrary to its intended purpose – such as ignoring security rules, disclosing sensitive information or transferring data outside the organization.
The most dangerous are indirect attacks, in which malicious code is hidden in a file, a fragment of a web page, or even an image. Such “silent sabotage" can be automatically triggered by AI, causing damage before anyone notices that the system is malfunctioning.
Not Just an IT Problem
AI-based solutions are increasingly integrated into business systems, databases or production processes. This means that a successful attack on an AI model can lead to the leakage of customer data or company secrets, or the manipulation of analysis results or recommendations. In extreme cases, key processes can even be halted. As a result, companies lose the trust of partners and customers, and their brand reputation suffers.
A New Generation of Security Testing
Traditional security methods – such as penetration tests or web application audits – do not address the specificity of language models. Therefore, organizations are increasingly turning to specialized LLM security testing, so-called LLM red teaming and prompt analysis, to detect and neutralize risks specific to AI environments.
If AI supports processes in your company, ensure comprehensive security testing of prompts and the LLM environment before an attacker does.