SNEAK: Evaluating Strategic Communication and Information Leakage in Large Language Models
Large language models (LLMs) are increasingly deployed in multi-agent settings where communication must balance informativeness and secrecy. In such settings, an agent may need to signal information to collaborators while preventing an adversary from inferring sensitive details. However, existing LLM benchmarks primarily evaluate capabilities such as reasoning, factual knowledge, or instruction following, and do not directly measure strategic communication under asymmetric information. We introduce SNEAK (Secret-aware Natural language Evaluation for Adversarial Knowledge), a benchmark for evaluating selective information sharing in language models. In SNEAK, a model is given a semantic ca...
| Authors | Adar Avsian, Larry Heck |
|---|---|
| Primary category | cs.CL |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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