World models learn internal representations of environment dynamics to predict future states, enabling agents to optimize action plans without physical interactions. However, developing world models that genuinely internalize underlying causal physical laws to explicitly reason about action preconditions and subsequent...
Jianan Wang, Hao-Quan Zhai, Si-Yang Zhang et al.· 0 citations
Large language models (LLMs) are increasingly used in software pipelines, raising concerns about harmful behaviors in security-critical domains. Existing safety evaluations predominantly probe models with single prompts or short interactions, and therefore do not capture how safety behaves under multi-step workflows wh...
Lu Yan, Zhuo Zhang, Xiang-Zhe Xu et al.· Proceedings of the ACM on So...· 0 citations
LLM agents can make unsafe tool calls even when instructed to behave safely. Existing defenses constrain agents before execution, modify tool inputs/outputs, or rely on LLM judges; these approaches may depend on model behavior or block unsafe actions without helping the agent recover. We argue that the execution enviro...
Charlie Summers, Prajwal Raghunath, Aaditya Pai et al.· 0 citations
FlowCheck, a constraint language to specify user-visible information flows directly through the application interface, where constraints can also be displayed and inspected without reading code, and are structured enough for reliable LLM generation.
Reya Vir, Lydia B. Chilton, Zhuo Zhang et al.· Proceedings of the 2nd ACM S...· 2 citations
SRE-Bench is introduced, the first realistic, contamination-free RE benchmark, and results indicate that strong source-code security capabilities do not yet transfer to binary analysis, highlighting RE as an important frontier for agentic cybersecurity and SRE-Bench as a rigorous testbed to measure progress.
J. Spence, Nicholas Assaderaghi, Feng Xiao et al.· 1 citation
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