BiRD: Race Detection in Software Binaries under Relaxed Memory Models

Jain, Ridhi; Purandare, Rahul; Sharma, Subodh

doi:10.1145/3498538

Cited by 3 publications

(2 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Binary memory dependence analysis, which determines whether two machine instructions in an executable can access the same memory location, is critical for many security-sensitive tasks, including detecting vulnerabilities [18,37,86], analyzing malware [39,95], hardening binaries [4,29,45,92], and forensics [19,35,58,93]. The key challenge behind memory dependence analysis is that machine instructions often leverage indirect addressing or indirect controlflow transfer (i.e., involving dynamically computed targets) to access the memory.…”

Section: Introductionmentioning

confidence: 99%

NeuDep: Neural Binary Memory Dependence Analysis

Pei,

She,

Wang

et al. 2022

Preprint

View full text Add to dashboard Cite

Determining whether multiple instructions can access the same memory location is a critical task in binary analysis. It is challenging as statically computing precise alias information is undecidable in theory. The problem aggravates at the binary level due to the presence of compiler optimizations and the absence of symbols and types. Existing approaches either produce significant spurious dependencies due to conservative analysis or scale poorly to complex binaries.We present a new machine-learning-based approach to predict memory dependencies by exploiting the model's learned knowledge about how binary programs execute. Our approach features (i) a self-supervised procedure that pretrains a neural net to reason over binary code and its dynamic value flows through memory addresses, followed by (ii) supervised finetuning to infer the memory dependencies statically. To facilitate efficient learning, we develop dedicated neural architectures to encode the heterogeneous inputs (i.e., code, data values, and memory addresses from traces) with specific modules and fuse them with a composition learning strategy.We implement our approach in NEUDEP and evaluate it on 41 popular software projects compiled by 2 compilers, 4 optimizations, and 4 obfuscation passes. We demonstrate that NEUDEP is more precise (1.5×) and faster (3.5×) than the current state-of-the-art. Extensive probing studies on security-critical reverse engineering tasks suggest that NEUDEP understands memory access patterns, learns function signatures, and is able to match indirect calls. All these tasks either assist or benefit from inferring memory dependencies. Notably, NEUDEP also outperforms the current state-of-the-art on these tasks.

show abstract

Section: Introductionmentioning

confidence: 99%

NeuDep: Neural Binary Memory Dependence Analysis

Pei,

She,

Wang

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

NeuDep: neural binary memory dependence analysis

Pei

She

Wang

et al. 2022

Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Enginee

View full text Add to dashboard Cite

Determining whether multiple instructions can access the same memory location is a critical task in binary analysis. It is challenging as statically computing precise alias information is undecidable in theory. The problem aggravates at the binary level due to the presence of compiler optimizations and the absence of symbols and types. Existing approaches either produce significant spurious dependencies due to conservative analysis or scale poorly to complex binaries.We present a new machine-learning-based approach to predict memory dependencies by exploiting the model's learned knowledge about how binary programs execute. Our approach features (i) a self-supervised procedure that pretrains a neural net to reason over binary code and its dynamic value flows through memory addresses, followed by (ii) supervised finetuning to infer the memory dependencies statically. To facilitate efficient learning, we develop dedicated neural architectures to encode the heterogeneous inputs (i.e., code, data values, and memory addresses from traces) with specific modules and fuse them with a composition learning strategy.We implement our approach in NeuDep and evaluate it on 41 popular software projects compiled by 2 compilers, 4 optimizations, and 4 obfuscation passes. We demonstrate that NeuDep is more precise (1.5×) and faster (3.5×) than the current state-of-the-art. Extensive probing studies on security-critical reverse engineering tasks suggest that NeuDep understands memory access patterns, learns function signatures, and is able to match indirect calls. All

show abstract

Diversity’s Double-Edged Sword: Analyzing Race’s Effect on Remote Pair Programming Interactions

Mason,

Kuttal

2024

ACM Trans. Softw. Eng. Methodol.

View full text Add to dashboard Cite

Remote pair programming is widely used in software development, but no research has examined how race affects these interactions between developers. We embarked on this study due to the historical under representation of Black developers in the tech industry, with White developers comprising the majority. Our study involved 24 experienced developers, forming 12 gender-balanced same- and mixed-race pairs. Pairs collaborated on a programming task using the think-aloud method, followed by individual retrospective interviews. Our findings revealed elevated productivity scores for mixed-race pairs, with no differences in code quality between same- and mixed-race pairs. Mixed-race pairs excelled in task distribution, shared decision-making, and role-exchange but encountered communication challenges, discomfort, and anxiety, shedding light on the complexity of diversity dynamics. Our study emphasizes race’s impact on remote pair programming and underscores the need for diverse tools and methods to address racial disparities for collaboration.

show abstract

BiRD: Race Detection in Software Binaries under Relaxed Memory Models

Cited by 3 publications

References 92 publications

NeuDep: Neural Binary Memory Dependence Analysis

NeuDep: Neural Binary Memory Dependence Analysis

NeuDep: neural binary memory dependence analysis

Diversity’s Double-Edged Sword: Analyzing Race’s Effect on Remote Pair Programming Interactions

Contact Info

Product

Resources

About