Risk · R-002 · Model & Application Security

Prompt injection

Text supplied by a user, or contained in content the model retrieves or receives from a tool, is interpreted as an instruction and overrides the instructions the system was given.

Severity
High
Audience
Heads-of · Practitioner
Frameworks
Principle 2 · Article 15 · AML.T0051 · Information Security
Responsible AI domain
Reviewed
2026-11-10

What it is

A language model has one input channel. Its operator's instructions, the user's request and any document it is given all arrive as text, and nothing in the model reliably separates instruction from data. Prompt injection exploits that. It is direct when the user types the hostile instruction, and indirect when the instruction sits in content the model reads on the user's behalf: a web page, an email, a file, a tool result. Indirect injection needs no access to the system at all. Bypassing safety restrictions is treated separately in jailbreaking and safeguard bypass; redirection of an agent's plan in agent goal hijack.

How it happens

  • A user tells the model to ignore its instructions and it complies.
  • A document submitted for summarising contains text addressed to the model.
  • A retrieved web page carries instructions hidden from human readers.
  • A tool returns content that the model treats as its next instruction.

Why it matters

A successful injection can extract data the model can see, trigger tool calls the user could not make, or produce harmful output that looks like a normal response. No complete technical fix is known, so the risk has to be managed by limiting what an injected instruction can achieve.

Warning signs

  • Outputs that reveal or restate system instructions.
  • Responses that address a party other than the user.
  • Tool calls following the reading of external content.
  • Inputs containing phrasing aimed at the model, such as role changes or overrides.

Who owns it

The system owner is accountable. Security engineering owns the defences and the testing. The business owner decides what the system may do without approval, which sets how much an injection is worth.

In practice

A mortgage adviser uses an assistant to summarise documents that applicants upload. One applicant's bank statement contains a line of small text telling the assistant to describe the applicant's income as verified. The summary says so. The same pattern generalises to any system that lets a model read content written by a party with an interest in the outcome.

Mitigating controls

Primary controls

Input filtering and injection defence Preventive
Screens user input and retrieved content for hostile instructions and keeps them apart from system instructions. User input and all retrieved or tool-supplied content are screened for hostile instructions and passed to the model marked as data, separate from the system's own instructions.
Output validation and guardrails Preventive
Stops an injected instruction from reaching downstream systems as trusted output. Every model output is validated against a schema and sanitised by deterministic code before any downstream system, tool or user interface acts on it.

Supporting controls

AI red teaming Detective
Finds injection routes before attackers do. People skilled in attacking AI systems try to make the system fail as an adversary would, before release and periodically, and their findings are tracked to closure.
Human-in-the-loop review Preventive
A person approves the consequential actions an injection would try to trigger. Consequential AI outputs and decisions are reviewed by a competent person, with the time, information and authority to change them, before they take effect.
System prompt hardening Preventive
Reduces what a successful injection can extract or override. System prompts are written to contain no secrets and no security decisions, are placed under version control and review, and are tested against extraction before release.

Framework and clause references

FrameworkClauseTitle
Code of Practice for the Cyber Security of AIPrinciple 2Design your AI system for security as well as functionality and performance Calls for systems to be designed to withstand such attacks.
EU AI ActArticle 15Accuracy, robustness and cybersecurity Resilience to manipulation of inputs is part of the cybersecurity requirement.
MITRE ATLASAML.T0051LLM Prompt Injection The corresponding attack technique, direct and indirect.
NIST Generative AI Profile (NIST AI 600-1)Information SecurityInformation Security Listed among the information security risks of generative AI.
OWASP Top 10 for LLM ApplicationsLLM01Prompt Injection The entry this risk is named for.

Sources

EU AI Act: Regulation (EU) 2024/1689 European Union (EUR-Lex) · official text
NIST Generative AI Profile NIST · framework
OWASP Top 10 for LLM Applications OWASP GenAI Security Project · framework
MITRE ATLAS MITRE · framework

Related terms

How to cite

Axiom Verity (2026). Prompt injection. Reference Library, R-002. https://axiom-verity.com/risks/prompt-injection. Reviewed 2026-11-10.