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What is prompt injection?
Prompt injection is an attack technique targeting AI language models, in which manipulative inputs cause the model to ignore or bypass its original instructions. The name is a play on “SQL injection”—in this case, prompts are manipulated instead of SQL commands.
Key attack types: Direct Prompt Injection (the attacker writes the malicious prompt themselves), Indirect Prompt Injection (malicious instructions in documents, web pages, or emails—which the AI incorporates while analyzing them), Jailbreak (bypassing the model’s security constraints), Data Exfiltration (extracting system prompts or RAG content).
Typical attack vectors: user chat (direct inputs into a chatbot), documents (hidden prompts in uploaded files), web content (prompts on web pages visited by AI agents), emails (for assistants with email access), tool outputs (for agent-based systems that use tools).
For small and medium-sized businesses, prompt injection is not a theoretical risk—attacks happen every day. This is especially true for customer-facing chatbots and custom assistants using RAG. Defense is multi-layered: technical (guardrails, filters), organizational (monitoring, incident response), and architectural (separation of user and system).
Best Practices for Preventing Prompt Injection
These six principles have proven effective:
- Never Trust User Input: All user input is potentially malicious—filter and restrict it.
- Use delimiters: XML tags or markers separate user context from system context.
- Least Privilege: AI has minimal privileges—critical actions require human approval.
- Regular Red Teaming: Actively search for vulnerabilities—don’t wait for an incident.
- Guardrails Framework: Don’t build it yourself—use proven solutions.
- Protect Sensitive Data: Do not include sensitive context in the model unless necessary.
Common Mistakes in Prompt Injection Defense
We frequently encounter these pitfalls:
- Relyingsolely on the system prompt for protection: Rules in the system prompt aren’t enough—they can be bypassed.
- Relying on external documents: PDFs and web pages can contain hidden prompts.
- No output filter: The model responds with system prompt content—leading to a data leak.
- Excessive Permissions: The AI agent is allowed too much—an attack can cause real damage.
- No monitoring: Attacks go undetected—they only come to light through user complaints.
Direct vs. Indirect vs. Multimodal Injection
Comparison of three attack types:
- Direct Injection: The attacker sends a malicious prompt directly to the model.
- Indirect Injection: The prompt is hidden in a document, webpage, or email—the model picks it up.
- Multimodal Injection: Attack via image, audio, or video—hidden instructions in media.
AI Security Against Prompt Injection
In a free initial consultation, we’ll assess your AI applications for prompt injection risks and outline a protection plan—guardrails, architecture, and monitoring.
As an AI partner for small and medium-sized businesses, we build secure AI applications with multi-layered defenses—from prompt design to red teaming.
What We Offer
- AI Consulting — Security Strategy and Implementation.
- AI Security — the comprehensive framework.
- Guardrails — the technical layers of protection.
- Prompt — the foundation of secure prompt design.
