Overview

AI prompt attacks: how they work and how to limit them

Text hidden in an email, page or file can steer an AI tool that reads it. No single patch removes the weakness, so the defence is limiting what your AI tools can see and do.

Last reviewed against NCSC (UK), OWASP Gen AI Security Project and UK Government (DSIT). Sources

At a glance

LLM01
OWASP's 2025 ranking for prompt injection among LLM application risks[4]
4
areas of NCSC advice: awareness, secure design, making attacks harder, monitoring[1]
3
capabilities that together let an agent be tricked into stealing data[6]
0
actions Microsoft said customers needed to take for the EchoLeak fix[7]

The latest

AI prompt attacks: what has happened so far

Newest first. Each entry links to its source.

Latest

OpenAI says AI browsers may always be exposed to prompt injection[9]

OpenAI said prompt injection is unlikely to ever be fully solved and that agent mode in ChatGPT Atlas expands the security threat surface. It described layered defences, including confirmation before sensitive actions.

NCSC warns of large-scale breaches if the problem is mistaken[2]

The NCSC said that treating prompt injection like SQL injection could leave websites open to data breaches larger than those of the 2010s, and advised reducing risk and impact.

NCSC says prompt injection may never be fully mitigated[1]

An NCSC blog argued that comparing prompt injection with SQL injection is dangerous, because language models do not enforce a boundary between instructions and data.

The full timeline

If your organisation runs, or plans to run, AI assistants, chatbots and agents, this is a guide to how attackers manipulate them. The evidence comes from NCSC publications, the OWASP project, the government's code of practice, security researchers and reporting on a Microsoft 365 Copilot flaw and on OpenAI's browser agent.

The problem

Instructions hidden in data

In the NCSC's words, an attacker crafts an input so that the model behaves in a way nobody intended [3]. That can mean offensive output, leaked confidential information, or actions taken in systems that trust their input [3]. OWASP puts prompt injection at the top of its 2025 ranking of LLM application risks, as LLM01 [4].

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Direct attacks

The person typing is the attacker. Their own input changes how the model behaves, whether on purpose or by accident [4]. Read what prompt injection is.

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Indirect attacks

The model reads a web page, file or email that contains instructions, and follows them [4]. The user may never see the text. The page on indirect prompt injection explains the routes in.

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Agents with tools

An AI agent that can send email or browse the web gives a hidden instruction something to act on. OpenAI said agent mode in ChatGPT Atlas "expands the security threat surface" [9]. See AI browser agents.

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A real flaw

Researchers showed that a crafted email could make Microsoft 365 Copilot leak data with no action from the user, a flaw called EchoLeak [7]. The EchoLeak page sets out what happened.

Why no patch

Not like SQL injection

In December 2025 the NCSC argued that comparing prompt injection with SQL injection is dangerous [1]. SQL injection can be properly mitigated with parameterised queries, whereas today's language models draw no enforced line between the instructions and the data in a prompt [1]. OpenAI takes a similar view of AI browsers: it called prompt injection unlikely to ever be fully "solved" [9].

What this means for you

The NCSC says to cut both the chance and the consequences of an attack. Where a system cannot live with whatever risk is left, a language model may be the wrong choice for that job [1].

First steps

Where to start

The exposure checklist walks through each of them. After that, the risk reduction plan covers the controls and the questions page gives short answers.

Begin with what your AI tools can reach, not with the attacker's wording. The three checks below take no special tooling:

  • List each AI tool and the content it reads from outside your organisation [4]
  • Find any tool that can also see private data and send information out [6]
  • Put a person or a fixed rule in front of any action that matters [4]

Am I affected?

Answer a few questions

Check if you are affected

Answer three questions about the AI tools your organisation uses. Nothing you answer leaves this page.

Does any AI tool or assistant read content that comes from outside your organisation, such as inbound email, web pages or uploaded documents?

Based on [4][6][1]. Nothing you enter leaves this page.

Quick answers

Questions people ask

What is prompt injection?

It is an attack where an input is crafted to make an AI model behave in an unintended way[3]. The results can include leaking confidential information or triggering actions in systems that accept unchecked input[3].

Can prompt injection be fixed?

Not completely, according to the NCSC. SQL injection can be properly mitigated, but language models do not enforce a boundary between instructions and data, so a complete mitigation may never be possible[1]. The practical aim is to make attacks less likely and less damaging.

What is indirect prompt injection?

It happens when a model reads external content, such as a web page or a file, that contains instructions it then follows[4]. The user typing the request is not the attacker.

What is the difference between prompt injection and a jailbreak?

OWASP describes jailbreaking as a related form in which the attacker aims to make the model disregard its safety protocols entirely[4]. Prompt injection is the wider term for input that changes a model's behaviour in unintended ways.

Every question, answered

About this site

Primary sources, dated

The CyPro Consulting Ltd team maintains this site. Every version, date and figure comes from 7 named sources, including NCSC (UK), OWASP Gen AI Security Project, UK Government (DSIT), and links to the one it came from. Last checked 4 November 2026.

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Know your exposure

Not sure you are protected?

Prompt injection cannot be patched away, so the useful question is how far a manipulated tool could reach. Testing your AI-connected systems the way an attacker would shows where the limits on data and actions need tightening.