Natural Language ProcessingSemantics & Representation

Prompt Injection

Overview

Direct Answer

Prompt injection is a security vulnerability in which an attacker embeds malicious instructions within user input to manipulate a language model into bypassing its original directives or system constraints. This technique exploits the model's inability to distinguish between legitimate user queries and adversarial instructions designed to override its intended behaviour.

How It Works

An attacker crafts input that includes hidden instructions, often using techniques such as context switching, role-playing prompts, or explicit directives prefixed with phrases like 'ignore previous instructions.' The language model processes this concatenated input sequentially and treats the injected content as legitimate guidance, causing it to prioritise the new instructions over its system-level constraints and training.

Why It Matters

Organisations deploying language models in customer-facing applications, content generation, and data analysis face significant risks including unauthorised data disclosure, brand reputation damage, and regulatory compliance violations. Teams must address this vulnerability to ensure model outputs remain trustworthy and aligned with business objectives and legal requirements.

Common Applications

Prompt injection affects chatbot applications, automated customer support systems, content management platforms, and AI-driven code generation tools. Attackers have demonstrated exploitation through email inputs to email-filtering systems and user prompts to customer service bots, revealing widespread exposure across enterprise deployments.

Key Considerations

Mitigation requires multi-layered defences including input sanitisation, model fine-tuning, and architectural separation of user input from system instructions. No single technical solution eliminates the risk entirely, necessitating ongoing monitoring and adversarial testing as attack methods evolve.

Cross-References(1)

Natural Language Processing

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