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SoK: A Tale of Reduction, Security, and Correctness - Evaluating Program Debloating Paradigms and Their Compositions

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Computer Security – ESORICS 2023 (ESORICS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14347))

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Abstract

Automated software debloating of program source or binary code has tremendous potential to improve both application performance and security. Unfortunately, measuring and comparing the effectiveness of various debloating methods is challenging due to the absence of a universal benchmarking platform that can accommodate diverse approaches. In this paper, we first present \({\textsc {DebloatBench}}_{\textrm{A}}\) (Debloating benchmark for applications), an extensible and sustainable benchmarking platform that enables comparison of different research techniques. Then, we perform a holistic comparison of the techniques to assess the current progress.

In the current version, we integrated four software debloating research tools: Chisel, Occam, Razor, and Piece-wise. Each tool is representative of a different class of debloaters: program source, compiler intermediate representation, executable binary, and external library. Our evaluation revealed interesting insights (i.e., hidden and explicit tradeoffs) about existing techniques, which might inspire future research. For example, all the binaries produced by Occam and Piece-Wise were correct, while Chisel significantly outperformed others in binary size and Gadget class reductions. In a first-of-its-kind composition, we also combined multiple debloaters to debloat a single binary. Our performance evaluation showed that, in both ASLR-proof and Turing-complete gadget expressively cases, several compositions (e.g., Chisel-Occam, Chisel-Occam-Razor) significantly outperformed the best-performing single tool (i.e., Chisel).

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Notes

  1. 1.

    Some of the test cases are taken from Razor Benchmarks [37].

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Acknowledgements

This material is based upon work supported by the National Science Foundation (NSF) under Grant ACI-1440800 and the Office of Naval Research (ONR) under Contracts N68335-17-C-0558 and N00014-18-1-2660. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF or ONR. We thank Muhammad Hassan, Abdullah Naveed, Talha Tahir, Muhammad Farrukh, and Ahsan Amin for their help in preparing and testing the large application suite.

Author information

Authors and Affiliations

  1. University of Arizona, Tucson, USA

    Muaz Ali & Sazzadur Rahaman

  2. Stony Brook University, Stony Brook, USA

    Muhammad Muzammil

  3. Georgia Institute of Technology, Atlanta, USA

    Faraz Karim

  4. Boston University, Boston, USA

    Ayesha Naeem

  5. Tufts University, Medford, USA

    Rukhshan Haroon

  6. LUMS, Lahore, Pakistan

    Muhammad Haris, Waseem Sabir & Fareed Zaffar

  7. University of Pittsburgh, Pittsburgh, USA

    Huzaifah Nadeem

  8. Data Security Technologies, Richardson, USA

    Fahad Shaon

  9. SRI International, Menlo Park, USA

    Vinod Yegneswaran & Ashish Gehani

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  1. Muaz Ali
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Corresponding author

Correspondence to Muaz Ali .

Editor information

Editors and Affiliations

  1. University of California, Irvine, CA, USA

    Gene Tsudik

  2. University of Padua, Padua, Italy

    Mauro Conti

  3. Delft University of Technology, Delft, The Netherlands

    Kaitai Liang

  4. Delft University of Technology, Delft, The Netherlands

    Georgios Smaragdakis

Appendix

Appendix

Table 3. The number of train and correctness testcases we used during our experiment for each of the input programs.
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Fig. 10.
figure 10

For coreutils, (a) average fractions of test passed for different heuristics in Razor and (b) relationship between the time taken to train Razor and the number of train cases. For non-coreutils, (c) ASLR-proof ROP gadget expressivity and (d) Turing-complete ROP gadget expressivity reduction.

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Ali, M. et al. (2024). SoK: A Tale of Reduction, Security, and Correctness - Evaluating Program Debloating Paradigms and Their Compositions. In: Tsudik, G., Conti, M., Liang, K., Smaragdakis, G. (eds) Computer Security – ESORICS 2023. ESORICS 2023. Lecture Notes in Computer Science, vol 14347. Springer, Cham. https://doi.org/10.1007/978-3-031-51482-1_12

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  • DOI: https://doi.org/10.1007/978-3-031-51482-1_12

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