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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Cognitive Threat Detection for SOC Operations: Automating Manipulation Tactic...

Keerthana Madhavan (School of Computer Science, University of Guelph, Canada), Luiza Antonie (School of Computer Science; CARE-AI, University of Guelph, Canada), Stacey D. Scott, School of Computer Science; CARE-AI, University of Guelph, Canada)

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Enhancing Semantic-Aware Binary Diffing with High-Confidence Dynamic Instruction Alignment

Chengfeng Ye (The Hong Kong University of Science and Technology, China), Anshunkang Zhou (The Hong Kong University of Science and Technology, China), Charles Zhang (The Hong Kong University of Science and Technology, China)

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Select-Then-Compute: Encrypted Label Selection and Analytics over Distributed Datasets...

Nirajan Koirala (University of Notre Dame), Seunghun Paik (Hanyang University), Sam Martin (University of Notre Dame), Helena Berens (University of Notre Dame), Tasha Januszewicz (University of Notre Dame), Jonathan Takeshita (Old Dominion University), Jae Hong Seo (Hanyang University), Taeho Jung (University of Notre Dame)

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