Geometry-Lite decomposes LLM safety detection into layer-wise margin geometries and finds that persistent boundary positions, not layer-to-layer drift, drive most detection performance across nine models and seven benchmarks.
When benchmarks lie: Evaluating malicious prompt classifiers under true distribu- tion shift
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Prompt injection detection performance is highly regime-dependent with no single detector dominating across settings; transformer models perform best overall while structural signals offer modest gains in some regimes.
citing papers explorer
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Geometry-Lite: Interpretable Safety Probing via Layer-Wise Margin Geometry
Geometry-Lite decomposes LLM safety detection into layer-wise margin geometries and finds that persistent boundary positions, not layer-to-layer drift, drive most detection performance across nine models and seven benchmarks.
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Prompt Injection Detection is Regime-Dependent: A Deployment-Aware Evaluation with Interpretable Structural Signals
Prompt injection detection performance is highly regime-dependent with no single detector dominating across settings; transformer models perform best overall while structural signals offer modest gains in some regimes.