MLLMs are increasingly deployed in user-facing applications, yet they inherit backdoor risks from the pipelines used to construct them: triggers may reside in images, texts, or both. Existing model-level backdoor removal methods, largely designed for conventional classifiers, show limited effectiveness on MLLMs, while...
Jia-Li Wei, Ming Fan, Ming-Kun Zhang et al.· 0 citations
GraphAHA is proposed, an adaptive graph search method with heterogeneous actions that organizes the test-time code generation in a typed directed acyclic graph and outperforms the strongest baseline for both models on both benchmarks by 4.1 percentage points on average.
Xi-Tao Li, Hai-Jun Wang, Ge-Ge Yuan et al.· 0 citations
Standardized token contracts (e.g., ERC-20) form the foundation of digital assets. However, attackers increasingly abuse this standardization to disguise malicious trap tokens. Unlike obvious violations, these contracts employ a strategy of"deceptive adherence": they strictly adhere to standard protocols to evade detec...
Yin Wu, Yi-Xuan Liu, Yi Li et al.· arXiv.org· 1 citation
RightsGen, a framework that leverages Large Language Models (LLMs) to automate the discovery and documentation of user rights declarations in Android applications, demonstrates high efficiency in dynamic testing and highlights the effectiveness of RightsGen in automating the generation of user rights declarations.
Chengjun Li, Tao Liu, Yi Wu et al.· SIGSOFT FSE Companion· 0 citations
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