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Tupni: automatic reverse engineering of input formats
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Conference on Computer and Communications Security archive
Proceedings of the 15th ACM conference on Computer and communications security table of contents
Alexandria, Virginia, USA
SESSION: System security 2 table of contents
Pages 391-402  
Year of Publication: 2008
ISBN:978-1-59593-810-7
Authors
Weidong Cui  Microsoft Research, Redmond, WA, USA
Marcus Peinado  Microsoft Corporation, Redmond, WA, USA
Karl Chen  University of California, Berkeley, CA, USA
Helen J. Wang  Microsoft Research, Redmond, WA, USA
Luis Irun-Briz  Microsoft Corporation, Redmond, WA, USA
Sponsors
ACM: Association for Computing Machinery
SIGSAC: ACM Special Interest Group on Security, Audit, and Control
Publisher
ACM  New York, NY, USA
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ABSTRACT

Recent work has established the importance of automatic reverse engineering of protocol or file format specifications. However, the formats reverse engineered by previous tools have missed important information that is critical for security applications. In this paper, we present Tupni, a tool that can reverse engineer an input format with a rich set of information, including record sequences, record types, and input constraints. Tupni can generalize the format specification over multiple inputs. We have implemented a prototype of Tupni and evaluated it on ten different formats: five file formats (WMF, BMP, JPG, PNG and TIF) and five network protocols (DNS, RPC, TFTP, HTTP and FTP). Tupni identified all record sequences in the test inputs. We also show that, by aggregating over multiple WMF files, Tupni can derive a more complete format specification for WMF. Furthermore, we demonstrate the utility of Tupni by using the rich information it provides for zero-day vulnerability signature generation, which was not possible with previous reverse engineering tools.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Weidong Cui: colleagues
Marcus Peinado: colleagues
Karl Chen: colleagues
Helen J. Wang: colleagues
Luis Irun-Briz: colleagues