Extract LLM System Prompt - AI Security Technique
AI Security TechniqueAdversaries may attempt to extract a large language model's (LLM) system prompt. This can be done via prompt injection to induce the model to reveal its own system prompt or may be extracted from a configuration file. System prompts can be a portion of an AI provider's competitive advantage and are thus valuable intellectual property that may be targeted by adversaries.
Overview
A source-backed snapshot of this AI security technique.
Technique details
Identifiers, maturity, and source taxonomy for this technique.
- ATLAS ID
- AML.T0056
- Maturity
- feasible
- Priority score
- 79
Attack flow
How to read the public records connected to this technique.
Impact
Why this technique may deserve attention in the current dataset.
- Evidence levelfeasible
- Mapped defenses3 ATLAS mitigation records
- Public examples0 linked case study records
- Research risks12 related MIT AI Risk records above the confidence threshold
- Vulnerabilities0 linked CVE records
Mitigations
Defenses that may help against this attack.
AML.M0020 - Generative AI Guardrails
Guardrails can prevent harmful inputs that can lead to meta prompt extraction.
AML.M0021 - Generative AI Guidelines
Model guidelines can instruct the model to refuse a response to unsafe inputs.
AML.M0022 - Generative AI Model Alignment
Model alignment can improve the parametric safety of a model by guiding it away from unsafe prompts and responses.
Case studies
Examples from public reports and exercises.
Source evidence
Original public records and references for this page.
Original source
Original source links
Open the public records and source datasets used for this page.