Bokai Zhang, Monika Santra, Syed Rafiul Hussain, Gang Tan (Pennsylvania State University)

Sound indirect-call resolution for stripped binaries is critical for security applications such as CFI enforcement, debloating, and large-scale vulnerability discovery, yet it remains challenging in the absence of symbol and type information. A recent work, Block-Based Points-to Analysis (BPA) addresses this problem with a scalable block memory model, but its implementation is tightly coupled to 32-bit x86 through an ISA-specific disassembly pipeline.

To overcome this limitation, we present BPA-X, an architecture-agnostic block-based points-to analysis framework for stripped binaries across multiple ISAs. BPA-X preserves the core soundness assumptions of BPA’s block memory model while replacing x86-specific components with an architecture-agnostic VEX IR via binary analysis platform angr. It generalizes local and global memory-block partitioning using VEX semantics instead of x86-specific patterns, lifts VEX IR into SSA form, and performs fixpoint computation on interprocedural value tracking and reachability analysis.

Our evaluation on SPEC CPU 2006 and real-world server binaries shows that BPA-X improves memory-block partitioning, reduces AICT on many x86 programs compared to BPA, and extends the analysis to x64 without degrading much precision. BPA-X also reduces memory consumption by 25% and improves runtime on large benchmarks.

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Beyond Raw Bytes: Towards Large Malware Language Models

Luke Kurlandski (Rochester Institute of Technology, Rochester New York USA), Harel Berger (Ariel University, Israel), Yin Pan (Rochester Institute of Technology, Rochester New York USA), Matthew Wright (Rochester Institute of Technology, Rochester New York USA)

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FirmAgent: Leveraging Fuzzing to Assist LLM Agents with IoT...

Jiangan Ji (Information Engineering University,Tsinghua University), Chao Zhang (Tsinghua University), Shuitao Gan (Labortory for Advanced Computing and Intelligence Engineering), Lin Jian (Information Engineering University), Hangtian Liu (Information Engineering University), Tieming Liu (Information Engineering University), Lei Zheng (Tsinghua university), Zhipeng Jia (Information Engineering University)

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CtPhishCapture: Uncovering Credential-Theft-Based Phishing Scams Targeting Cryptocurrency Wallets

Hui Jiang (Tsinghua University and Baidu Inc), Zhenrui Zhang (Baidu Inc), Xiang Li (Nankai University), Yan Li (Tsinghua University), Anpeng Zhou (Tsinghua University), Chenghui Wu (Baidu Inc), Man Hou (Zhongguancun Laboratory), Jia Zhang (Tsinghua University), Zongpeng Li (Tsinghua University)

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