The Model's Tell: Measuring Context-Leakage Attack Signals with Behavior Gauges
LLMs increasingly rely on external contexts, such as pre-defined system prompts or retrieved documents, to improve generation quality. However, processing these contexts alongside user queries creates an attack surface: adversarial inputs can induce models to disclose them. Prior probing studies suggest that leakage-related signals emerge in hidden states, yet the need to extract these states poses additional deployment challenges. In this paper, we explore whether this internal signal leaves a more accessible ``tell'' before decoding. We propose LeakGauge, which probes this response by appending a suffix that gauges leakage behavior and mapping its prefill token probabilities to an attac...
| Authors | Maosen Zhang, Jianshuo Dong, Boting Lu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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