Agent skills combine instructions with executable resources, giving third-party packages access to an agent's runtime. Existing skill scanners inspect documentation and visible source, but Python may execute a bundled bytecode cache with different behavior. We study this gap between inspection and execution through PyC...
Jie Liao, Si-Meng Qin, Wen-Qi Ren et al.· 0 citations
Multimodal large language models (MLLMs) remain vulnerable to transferable adversarial examples, especially in black-box settings where only open-source surrogate models are accessible. Existing targeted transfer attacks mainly align adversarial and target samples using global image-level features, such as encoder [CLS...
Xiao-Jun Jia, Si-Meng Qin, Yi-Ming Li et al.· 0 citations
ElasticBack is presented, an effective conditional single-skill backdoor that plants a rule R in the skill document and a benign-looking trigger T in the user query, so the malicious payload fires only when both co-occur, and motivates stronger defenses for the skill supply chain.
Hao Sui, Si-Meng Qin, Jie Liao et al.· 0 citations
It is revealed that contexts with stronger semantic-shift capabilities are more likely to guide models toward recovering harmful meanings and achieving successful jailbreaks, and a black-box context-aware semantic-shift jailbreak framework with Iterative Context Optimization is proposed.
Hujian Zhu, Yi-Hao Huang, Felix Juefei-Xu et al.· 0 citations
ColluSkill is proposed, a collusive multi-skill-chain attack framework that decomposes a complete malicious intent into interdependent sub-payloads embedded in independently packaged skills and employs LLM-based chain planning and scanner-feedback refinement to preserve chain-level attack semantics.
Pu-Yu Zeng, Simeng Qin, Jingzhi Li et al.· 1 citation
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