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cs.CR2026
AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy Probes
Xun Wang, Bihe Zhao, Michael Backes +2
Commercial LLM APIs advertise a specific foundation model, but the served backbone may be silently substituted, quantized, or wrapped, for example to save deployment costs. All exi…
cs.CV2026
MultiMem: Measuring and Mitigating Memorization in Multi-Modal Contrastive Learning
Wenhao Wang, Franziska Boenisch, Michael Backes +1
Memorization in machine learning models enables high performance on rare in-distribution samples by capturing their atypical patterns. However, it also causes harmful retention of…
cs.CL2026
SafeReview: Defending LLM-based Review Systems Against Adversarial Hidden Prompts
Yuan Xin, Yixuan Weng, Minjun Zhu +5
As Large Language Models (LLMs) are increasingly integrated into academic peer review, their vulnerability to adversarial hidden prompts, i.e., adversarial instructions embedded in…