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Oct 12 · Monday

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Found 1 paper
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Attack type

Prompt injection3Extraction and theft1

Defense type

Inference-time intervention1

Layer

Model layer4Application layer1
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Attack type：Extraction and theftDefense type：Inference-time interventionLayer：Application layerClear all

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  1. DefensesarXiv 2606.18673

    Understanding and mitigating prompt leakage attacks in real-world LLM applications

    Propose soft prompt defense AREA, re-anchoring attention to mitigate system prompt leakage

    arXiv
    2606.18673
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Attacker
    Black-box
    Evaluated models
    Llama-2-7B-chat-hf, Llama-3.1-8B-Instruct, Mistral-7B-Instruct-v0.3 and others8
    AbstractThe authors measure commercial prompt leakage, propose attention drift, and use AREA to strike a trade-off between usability and leakage resistance.

    The authors study system prompt leakage in real LLM applications and propose AREA, a mitigation method that does not modify weights.

    Paper analysisRead analysis
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