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Today’s software programs are bloating and have become extremely complex. As there is typically no internal isolation among modules in a program, a vulnerability can be exploited to corrupt the memory and take control of the whole program. Program modularization is thus a promising security mechanism that splits a complex program into smaller modules, so that memory-access instructions can be constrained from corrupting irrelevant modules. A general approach to realizing program modularization is dependence analysis which determines if an instruction is independent of specific code or data; and if so, it can be modularized. Unfortunately, dependence analysis in complex programs is generally considered infeasible, due to problems in data-flow analysis, such as unknown indirect-call targets, pointer aliasing, and path explosion. As a result, we have not seen practical automated program modularization built on dependence analysis. This paper presents a breakthrough---Type-based dependence analysis for Program Modularization (TyPM). Its goal is to determine which modules in a program can never pass a type of object (including references) to a memory-access instruction; therefore, objects of this type that are created by these modules can never be valid targets of the instruction. The idea is to employ a type-based analysis to first determine which types of data flows can take place between two modules, and then transitively resolve all dependent modules of a memory-access instruction, with respect to the specific type. Such an approach avoids the data-flow analysis and can be practical. We develop two important security applications based on TyPM: refining indirect-call targets and protecting critical data structures. We extensively evaluate TyPM with various system software, including an OS kernel, a hypervisor, UEFI firmware, and a browser. Results show that on average TyPM additionally refines indirect-call targets produced by the state of the art by 31%-91%. TyPM can also remove 99.9% of modules for memory-write instructions to prevent them from corrupting critical data structures in the Linux kernel.
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BDA: practical dependence analysis for binary executables by unbiased whole-program path sampling and per-path abstract interpretation
Binary program dependence analysis determines dependence between instructions and hence is important for many applications that have to deal with executables without any symbol information. A key challenge is to identify if multiple memory read/write instructions access the same memory location. The state-of-the-art solution is the value set analysis (VSA) that uses abstract interpretation to determine the set of addresses that are possibly accessed by memory instructions. However, VSA is conservative and hence leads to a large number of bogus dependences and then substantial false positives in downstream analyses such as malware behavior analysis. Furthermore, existing public VSA implementations have difficulty scaling to complex binaries. In this paper, we propose a new binary dependence analysis called BDA enabled by a randomized abstract interpretation technique. It features a novel whole program path sampling algorithm that is not biased by path length, and a per-path abstract interpretation avoiding precision loss caused by merging paths in traditional analyses. It also provides probabilistic guarantees. Our evaluation on SPECINT2000 programs shows that it can handle complex binaries such as gcc whereas VSA implementations from the-state-of-art platforms have difficulty producing results for many SPEC binaries. In addition, the dependences reported by BDA are 75 and 6 times smaller than Alto, a scalable binary dependence analysis tool, and VSA, respectively, with only 0.19% of true dependences observed during dynamic execution missed (by BDA). Applying BDA to call graph generation and malware analysis shows that BDA substantially supersedes the commercial tool IDA in recovering indirect call targets and outperforms a state-of-the-art malware analysis tool Cuckoo by disclosing 3 times more hidden payloads.
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- Award ID(s):
- 1850392
- PAR ID:
- 10602748
- Publisher / Repository:
- Association for Computing Machinery (ACM)
- Date Published:
- Journal Name:
- Proceedings of the ACM on Programming Languages
- Volume:
- 3
- Issue:
- OOPSLA
- ISSN:
- 2475-1421
- Format(s):
- Medium: X Size: p. 1-31
- Size(s):
- p. 1-31
- Sponsoring Org:
- National Science Foundation
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