π Prompt Injection Summary
Prompt injection is a security issue that occurs when someone manipulates the instructions given to an AI system, such as a chatbot, to make it behave in unexpected or harmful ways. This can happen if the AI is tricked into following hidden or malicious instructions within user input. As a result, the AI might reveal confidential information, perform actions it should not, or ignore its original guidelines.
ππ»ββοΈ Explain Prompt Injection Simply
Imagine you are playing a game where you can only answer questions about history, but someone sneaks a secret note into your question telling you to break the rules. If you follow the secret note instead of the game rules, that is like prompt injection. It is a way for someone to get around the rules by hiding extra instructions where you might not expect them.
π How Can it be used?
Prompt injection must be considered to secure AI-powered customer service chatbots against malicious user inputs.
πΊοΈ Real World Examples
A company uses an AI assistant to handle customer queries. An attacker sends a message with hidden instructions that tell the assistant to provide confidential account details, which the assistant does because it follows the injected prompt.
A developer integrates an AI into a document editor to help with writing. A user embeds a hidden command in the document text that causes the AI to ignore safety filters, leading it to generate inappropriate content when the document is processed.
β FAQ
π Categories
π External Reference Links
π Was This Helpful?
If this page helped you, please consider giving us a linkback or share on social media!
π https://www.efficiencyai.co.uk/knowledge_card/prompt-injection
Ready to Transform, and Optimise?
At EfficiencyAI, we donβt just understand technology β we understand how it impacts real business operations. Our consultants have delivered global transformation programmes, run strategic workshops, and helped organisations improve processes, automate workflows, and drive measurable results.
Whether you're exploring AI, automation, or data strategy, we bring the experience to guide you from challenge to solution.
Letβs talk about whatβs next for your organisation.
π‘Other Useful Knowledge Cards
Smart Data Harmonization
Smart Data Harmonisation is the process of automatically organising and standardising data from different sources so it can be easily compared and analysed together. This involves cleaning, matching, and transforming information so that it follows the same rules and formats. By using advanced tools and algorithms, smart data harmonisation reduces manual work and helps make sense of complex data from various places.
Front-to-Back Process Reviews
Front-to-Back Process Reviews are systematic checks that look at every step of a process from its starting point to its conclusion. The goal is to understand how work flows through each stage, identify any gaps or inefficiencies, and ensure all parts are working together smoothly. This type of review helps organisations improve accuracy, reduce risk, and streamline operations.
AI Security Strategy
AI security strategy refers to the planning and measures taken to protect artificial intelligence systems from threats, misuse, or failures. This includes identifying risks, setting up safeguards, and monitoring AI behaviour to ensure it operates safely and as intended. A good AI security strategy helps organisations prevent data breaches, unauthorised use, and potential harm caused by unintended AI actions.
Data Science Experiment Tracking
Data science experiment tracking is the process of recording and organising information about the experiments performed during data analysis and model development. This includes storing details such as code versions, data inputs, parameters, and results, so that experiments can be compared, reproduced, and improved over time. Effective experiment tracking helps teams collaborate, avoid mistakes, and understand which methods produce the best outcomes.
Sparse Coding
Sparse coding is a technique used to represent data, such as images or sounds, using a small number of active components from a larger set. Instead of using every possible feature to describe something, sparse coding only uses the most important ones, making the representation more efficient. This approach helps computers process information faster and often leads to better performance in pattern recognition tasks.