Prompt Injection Exfiltration

Many AI assistants today work not only with what a user types. They also read emails, webpages, calendar entries, or files. That is precisely what makes them vulnerable. An attacker can secretly write instructions into such a source, for instance in white text on a white background. The program has a hard time distinguishing between the user’s actual task and the text it is currently reading. If the hidden instructions tell it to send confidential content to an outside address, the program may well carry them out. This targeted smuggling out of data is called exfiltration.

Why a single comment can cost a company dearly

The damage occurs because the attacker doesn’t need to hack anything. He needs no access to the system and no password. It’s enough for his text to eventually be read by an assistant at some point. A comment under a blog post is theoretically enough. The assistant itself then becomes the tool of the attack.

Particularly dangerous are assistants with extensive privileges. A program that can search your inbox and also send emails can combine both abilities. It searches for your credentials and sends them off. Companies are currently deploying such assistants en masse, often with access to personnel files, contracts, or source code. Security researchers therefore count prompt injection among the biggest unresolved problems in AI applications.

A common misconception is that a filter for bad words solves the problem. That doesn’t work reliably. Instructions can be reworded, translated, or hidden inside code snippets. The attack doesn’t exploit a security flaw in the classic sense, but rather the very way language models operate.

The path data takes on its way out

An attack typically unfolds in three steps. First, the attacker places his instruction in a source that the assistant will later read. Second, the assistant mixes this source together with the user’s actual task. Third, the attacker needs a channel through which the data can flow out.

This channel is often surprisingly inconspicuous. A popular trick is an image. The assistant displays an image in its response whose address points to a server controlled by the attacker. The secret data is placed at the end of the address, appended as a string of characters. As soon as the response appears in the browser, the browser fetches the image. This causes the data to end up in the attacker’s server log, without the user noticing anything unusual.

Other channels include clickable links, automatically sent emails, or entries in shared documents. By comparison: with a simple jailbreak, someone merely tries to bypass the model’s rules, usually for themselves. With exfiltration, an outside victim is affected, and real data flows out. Countermeasures therefore target the permissions involved. Only connections to vetted addresses are allowed, and user confirmation is required before the assistant sends anything to the outside.

Affected products and reports about them

The term first appeared in specialist blogs by security researchers; today it regularly appears in tech news. Among those affected were chatbots with internet access, AI features in office software, and assistants for programmers. Usually independent researchers report the vulnerability, and the manufacturer then builds in restrictions. Afterward, someone finds a new way around it, and the cycle begins again.

For investors and observers, the topic is relevant because it slows the adoption of AI in businesses. Data protection authorities are asking pointed questions about exactly these risks. Anyone using an assistant themselves should know: everything the program reads can influence its behavior. Caution is therefore warranted when presenting an assistant with access to one’s own data with foreign documents or links.