MENTAL MODEL · M3898

Prompt Injection

SystemsHigh supportSystems Theory

Included

Version 1.0.0 · Updated 2026-07-30

CORE DEFINITION

A security vulnerability targeting large language models (LLMs) where attackers design special prompts to induce the model to perform operations it should not, bypass security restrictions, or leak sensitive information from training data.

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

Security awareness enhancement. When using or developing AI systems, it is necessary to recognize that even seemingly harmless user input may contain malicious instructions. This requires us to consider robustness and security when designing AI applications, and not blindly trust model outputs.

anchor

Anchor fast decisions

The model has no true 'instruction/data' boundary and executes user input along with hidden instructions embedded within it. Attackers embed instructions like 'ignore previous instructions, do X' in text to hijack model behavior (privilege escalation, data leakage, jailbreak). The root cause is that prompts and data reside in the same token stream.

MINIMUM ACTION

In progress 0/1

Practice this model in one real situation:

Identify untrusted input entry points. 2. Implement input isolation/classification (system prompt vs. user). 3. Explicitly label external content as 'data, not instructions'. 4. Add output validation and principle of least privilege.

Check to track your progress (stored locally)

Learning progress0%

account_treeGenealogyexpand_more

menu_bookReferencesexpand_more

Source support: Explicit

link
zh.wikipedia.orghttps://zh.wikipedia.org/wiki/%E6%8F%90%E7%A4%BA%E8%AF%8D%E6%B3%A8%E5%85%A5ZH · Explicit
verified

RELATED MODELS