Summary: Inspired by Finding 8 of *Look Before You Leap*, this work introduces a lightweight, black-box paradigm for Code LLM risk assessment that shifts uncertainty estimation from surface-level textual similarity to execution semantics. Based on the observation that syntactic consensus can obscure substantial functional divergence, this work proposes grounding model confidence in the runtime behavior of generated programs as a promising direction for building more trustworthy code automation.

Summary: We propose characterizing LLM behavior unsupervisedly by jointly analyzing internal neuron-level representations and external uncertainty signals. By capturing complementary behavioral cues, this approach enables more effective and sample-efficient testing.

Summary: The internal hidden states of a code model contain critical signals regarding the trustworthiness of its outputs. However, effectively leveraging these signals from modern, massive LLMs requires an approach that matches their sheer complexity. Because a model's generative power emerges from massive scale, we argue that its risk assessment framework should evolve to match this by applying scalable pre-training approaches to these highly informative internal layers.

Summary: This work represents an early exploratory work that leverages external uncertainty analysis to evaluate model trustworthiness, highlighting that the open-ended, generative mechanisms of LLMs require fundamentally different uncertainty measurements than the fixed-class probability outputs of classical DNNs.

Summary: This work demonstrates that for DNN models (more specifically, Transformer), systematic and rigorous exhaustive testing can move beyond empirical evaluations to provide verifiable, certified results regarding a model's robustness against patch attacks.

Summary: This work performed a large-scale measurement study of the EOSIO ecosystem through graph analysis. It reveals EOSIO's superficial prosperity and pervasive malicious activities.