insight - Software Development - # Binary Code Search

BINENHANCE: Enhancing Binary Code Search with External Environment Semantics

Q: Could the reliance on static analysis for constructing the EESG be a limitation in scenarios where dynamic analysis is crucial for understanding function behavior?

You are right to point out that BINENHANCE's reliance on static analysis for EESG construction could be a limitation in scenarios where dynamic analysis is essential. Limitations of Static Analysis: Dynamically Loaded Code: Static analysis struggles to analyze code loaded or generated at runtime, which is common in malware and obfuscated software. Indirect Control Flow: Techniques like function pointers and dynamic dispatch make it difficult to statically determine the actual control flow, impacting the accuracy of CD edges. Context-Dependent Behavior: Some functions exhibit behavior dependent on runtime inputs or the program's state, which static analysis cannot fully capture. Incorporating Dynamic Analysis: Hybrid EESG Construction: Combine static analysis with dynamic analysis results. For instance, instrument binaries to log function calls during execution and use this information to refine the CD edges in the EESG. Dynamically-Informed Node Embeddings: Instead of relying solely on static features, augment function node embeddings with information derived from dynamic analysis. This could include function call frequencies, execution traces, or accessed memory regions. EESG Evolution: Treat the EESG as a dynamic structure that evolves as more dynamic analysis data becomes available. Update edge weights or add new edges based on observed function interactions. Trade-offs and Considerations: Performance Overhead: Dynamic analysis is significantly more time-consuming than static analysis. Carefully selecting analysis targets and techniques is crucial. Environment Dependence: Dynamic analysis results can be influenced by the specific execution environment. Strategies are needed to generalize findings.

Q: If code obfuscation techniques are employed to intentionally mask code similarity, how might BINENHANCE's effectiveness be impacted, and what strategies could be explored to mitigate these challenges?

Code obfuscation techniques, designed to make code harder to understand and analyze, pose a direct challenge to BINENHANCE's effectiveness. Obfuscation Impacts on BINENHANCE: Control Flow Obfuscation: Techniques like opaque predicates, code transposition, and control flow flattening disrupt the normal control flow, making it difficult to construct accurate CFGs and CD edges. Data Obfuscation: Encrypting strings, using packing techniques, or employing dynamic data structures can hinder the extraction of meaningful data features and the creation of DCU edges. Address Obfuscation: Techniques like address space layout randomization (ASLR) and code packing can alter function addresses, impacting the reliability of AA edges. Mitigation Strategies: Obfuscation-Resilient Features: Explore features less susceptible to obfuscation. For example, instead of relying on raw instruction sequences, consider using instruction histograms or control flow graph metrics. Deobfuscation Techniques: Integrate deobfuscation techniques as a preprocessing step. This could involve identifying and removing junk code, unpacking data, or resolving indirect control flow. Semantic-Aware Similarity Metrics: Develop similarity metrics that go beyond syntactic comparisons. Focus on identifying semantic equivalence even if the code structure is heavily obfuscated. Adversarial Training: Train BINENHANCE on datasets containing both obfuscated and unobfuscated code. This can help the model learn to generalize and recognize obfuscation patterns. Key Considerations: Obfuscation Arms Race: New obfuscation techniques are constantly emerging. BINENHANCE needs to adapt and evolve to stay effective. False Positives: Aggressive deobfuscation or overly relaxed similarity metrics might lead to an increase in false positives. Balancing accuracy is essential.

Core Concepts

BINENHANCE is a novel framework that leverages external environment semantics to improve the accuracy and efficiency of binary code search, particularly in challenging scenarios like function inlining and large-scale codebases.

Abstract

Bibliographic Information: Wang, Y., Li, H., Zhu, X., Li, S., Dong, C., Yang, S., & Qin, K. (2025). BINENHANCE: A Enhancement Framework Based on External Environment Semantics for Binary Code Search. Network and Distributed System Security (NDSS) Symposium 2025, San Diego, CA, USA.
Research Objective: This paper introduces BINENHANCE, a novel framework designed to enhance the accuracy and efficiency of binary code search by incorporating external environment semantics into existing internal code semantic models.
Methodology: BINENHANCE constructs an External Environment Semantic Graph (EESG) to capture inter-function relationships beyond traditional function call graphs. It utilizes four novel edge types: Call-Dependency, Data-Co-Use, Address-Adjacency, and String-Use. A Semantic Enhancement Model (SEM) based on Relational Graph Convolutional Networks (RGCNs) learns valuable external semantics from the EESG, enhancing the initial function embeddings generated by existing internal code semantic models. Additionally, BINENHANCE incorporates data feature similarity to refine the final similarity scores.
Key Findings: Extensive experiments on public datasets demonstrate that BINENHANCE significantly improves the performance of various state-of-the-art binary code search methods, achieving an average MAP improvement of 16.1%. It proves particularly effective in handling challenges like function inlining and large-scale codebases, showcasing its robustness and scalability.
Main Conclusions: BINENHANCE effectively addresses the limitations of existing binary code search methods by incorporating external environment semantics. This approach enhances the representation of function semantics, leading to improved accuracy and efficiency in identifying homologous functions.
Significance: This research highlights the importance of external environment semantics in binary code analysis and provides a practical framework for integrating this information into existing tools. It has significant implications for various security applications, including software reuse detection, vulnerability identification, and firmware analysis.
Limitations and Future Research: While BINENHANCE demonstrates promising results, future research could explore the integration of more sophisticated GNN architectures and the development of novel external environment features to further enhance its performance.

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Stats

The application of BINENHANCE to HermesSim, Asm2vec, TREX, Gemini, and Asteria on two public datasets results in an improvement of Mean Average Precision (MAP) from 53.6% to 69.7%.
The efficiency increases fourfold.
Compiler-caused function inlining can reach up to 70%.
In a pool of 10,000 functions, the time required to recall all the homologous functions of a binary file containing around 2500 functions is around 22 minutes using TREX’s 768-dimension embeddings.
128-dimension embeddings only require 5 minutes.
96% of 1703 software projects utilize open-source code.
84% of the 1703 software projects have at least one known vulnerability.

Quotes

"Binary code search plays a crucial role in applications like software reuse detection, and vulnerability identification."
"Firstly, the internal code semantics of functions may exhibit substantial variations due to different compilation settings, encompassing factors like function inlining and splitting."
"Secondly, exclusive reliance on function call graphs (CG) for assistance is insufficient for addressing complex real-world scenarios."
"Thirdly, current solutions exhibit limited scalability and struggle to cope with large-scale function search tasks."

Key Insights Distilled From

BinEnhance: A Enhancement Framework Based on External Environment Semantics for Binary Code Search

by Yongpan Wang... at arxiv.org 11-05-2024

https://arxiv.org/pdf/2411.01102.pdf

BinEnhance: A Enhancement Framework Based on External Environment Semantics for Binary Code Search

Deeper Inquiries

How can BINENHANCE be adapted to address the challenges of identifying code similarity in the context of cross-platform binary code analysis?

Adapting BINENHANCE for cross-platform binary code similarity analysis, where binaries might originate from different architectures (like x86, ARM) or operating systems (Windows, Linux), presents significant challenges and opportunities. Here's a breakdown of potential adaptations:
Challenges:

Instruction Set Variations: Different architectures have distinct instruction sets, impacting the structure of Control Flow Graphs (CFGs) and the semantics of function calls.
Calling Conventions:  Function call mechanisms, including parameter passing and stack management, vary across platforms, influencing the Data-Co-Use (DCU) edges in the EESG.
Library Function Divergence:  Even common library functions (e.g., printf) might have different implementations and call graphs on different platforms, affecting the Call-Dependency (CD) edges.
Adaptation Strategies:

Intermediate Representation (IR) Level Analysis: Instead of relying on platform-specific assembly code, lifting binaries to a platform-agnostic IR (like LLVM IR) can provide a common ground for analysis. BINENHANCE's EESG construction and SEM could operate on this IR, making it more portable.

Architecture-Specific EESG Edges: Introduce edge types in the EESG that capture platform-specific semantics. For example, edges could represent differences in calling conventions or the usage of architecture-specific registers.

Transfer Learning with Cross-Platform Datasets: Train BINENHANCE on datasets containing homologous functions compiled for different platforms. This could enable the model to learn cross-platform semantic mappings.

Homology Refinement with Symbolic Execution:  Employ symbolic execution to analyze function behavior across platforms. This can help identify semantically similar functions even if their low-level implementations differ significantly.

Key Considerations:

Performance Overhead:  IR lifting and symbolic execution can be computationally expensive. Efficient techniques are needed to manage this overhead.
Accuracy Trade-offs:  Abstracting away platform-specific details might lead to a loss of precision in some cases. Balancing accuracy and portability is crucial.

Could the reliance on static analysis for constructing the EESG be a limitation in scenarios where dynamic analysis is crucial for understanding function behavior?

You are right to point out that BINENHANCE's reliance on static analysis for EESG construction could be a limitation in scenarios where dynamic analysis is essential.
Limitations of Static Analysis:

Dynamically Loaded Code:  Static analysis struggles to analyze code loaded or generated at runtime, which is common in malware and obfuscated software.
Indirect Control Flow:  Techniques like function pointers and dynamic dispatch make it difficult to statically determine the actual control flow, impacting the accuracy of CD edges.
Context-Dependent Behavior:  Some functions exhibit behavior dependent on runtime inputs or the program's state, which static analysis cannot fully capture.
Incorporating Dynamic Analysis:

Hybrid EESG Construction: Combine static analysis with dynamic analysis results. For instance, instrument binaries to log function calls during execution and use this information to refine the CD edges in the EESG.

Dynamically-Informed Node Embeddings:  Instead of relying solely on static features, augment function node embeddings with information derived from dynamic analysis. This could include function call frequencies, execution traces, or accessed memory regions.

EESG Evolution:  Treat the EESG as a dynamic structure that evolves as more dynamic analysis data becomes available. Update edge weights or add new edges based on observed function interactions.

Trade-offs and Considerations:

Performance Overhead:  Dynamic analysis is significantly more time-consuming than static analysis. Carefully selecting analysis targets and techniques is crucial.
Environment Dependence:  Dynamic analysis results can be influenced by the specific execution environment. Strategies are needed to generalize findings.

If code obfuscation techniques are employed to intentionally mask code similarity, how might BINENHANCE's effectiveness be impacted, and what strategies could be explored to mitigate these challenges?

Code obfuscation techniques, designed to make code harder to understand and analyze, pose a direct challenge to BINENHANCE's effectiveness.
Obfuscation Impacts on BINENHANCE:

Control Flow Obfuscation: Techniques like opaque predicates, code transposition, and control flow flattening disrupt the normal control flow, making it difficult to construct accurate CFGs and CD edges.
Data Obfuscation:  Encrypting strings, using packing techniques, or employing dynamic data structures can hinder the extraction of meaningful data features and the creation of DCU edges.
Address Obfuscation:  Techniques like address space layout randomization (ASLR) and code packing can alter function addresses, impacting the reliability of AA edges.
Mitigation Strategies:

Obfuscation-Resilient Features:  Explore features less susceptible to obfuscation. For example, instead of relying on raw instruction sequences, consider using instruction histograms or control flow graph metrics.

Deobfuscation Techniques:  Integrate deobfuscation techniques as a preprocessing step. This could involve identifying and removing junk code, unpacking data, or resolving indirect control flow.

Semantic-Aware Similarity Metrics:  Develop similarity metrics that go beyond syntactic comparisons. Focus on identifying semantic equivalence even if the code structure is heavily obfuscated.

Adversarial Training:  Train BINENHANCE on datasets containing both obfuscated and unobfuscated code. This can help the model learn to generalize and recognize obfuscation patterns.

Key Considerations:

Obfuscation Arms Race:  New obfuscation techniques are constantly emerging. BINENHANCE needs to adapt and evolve to stay effective.
False Positives:  Aggressive deobfuscation or overly relaxed similarity metrics might lead to an increase in false positives. Balancing accuracy is essential.