Specialized Cybersecurity & Cognitive Security
Prompt Injection Defense in Depth
Prompt injection is the SQL injection of the AI era: untrusted text in a prompt can override instructions, leak context, or trigger tool calls the developer never intended. Defending against it means treating every external input as hostile, separating instructions from data, and layering filters, least-privilege tools, and output checks. No single layer is enough, because attackers adapt as fast as defenses ship.
Points clés
- Separate system instructions from user and retrieved content using clear delimiters and structured formats.
- Validate and sanitize retrieved documents and tool outputs before they enter the prompt context.
- Give the model the least-privilege tool set, and require confirmation for sensitive actions.
- Apply input and output filters that detect instruction overrides, jailbreaks, and secret leakage.
- Log prompts and tool calls so injected behavior can be traced and rolled back.
- Red-team prompts with adversarial datasets covering direct, indirect, and multi-turn injection.
- Keep secrets and production data out of prompts; use scoped tokens and synthetic fixtures.
- Fail closed: if a filter or tool check is uncertain, block the action rather than allow it.
Questions fréquentes
Can prompt injection be fully prevented?
No. Layered defenses raise the cost and reduce impact, but a determined attacker can still find gaps.
Is output filtering enough on its own?
No. It catches leakage but not unauthorized tool calls or logic changes hidden in the answer.
What is indirect prompt injection?
Malicious instructions hidden in data the model retrieves, such as a web page or document.
Where should defenses live?
At every boundary: ingestion, prompt assembly, tool execution, and output rendering.
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