Automated Exploit Generation

Automated Exploit Generation is a computational methodology in cybersecurity research that systematically synthesizes working exploits for identified software vulnerabilities without manual intervention, distinguishing itself from manual or semi-automated approaches by its reliance on formal models, symbolic execution, or program analysis techniques to fully automate exploit construction.

50

Publications

3.3K

Citations

142

Authors

58

Institutions

Publications per year

2017–2026

24

Authors

142

  1. DB
    David Brumley

    Carnegie Mellon University, United States

    8 publications · 1.5K citations

    8

    h-index

  2. DS
    Dawn Song

    University of California, Berkeley, United States

    4 publications · 420 citations

    4

    h-index

  3. SH
    Sean Heelan

    ImmunityBio (United States), United States

    4 publications · 97 citations

    4

    h-index

  4. VN
    V. N. Venkatakrishnan

    University of Illinois Chicago, United States

    3 publications · 83 citations

    3

    h-index

  5. DK
    Daniel Kroening

    University of Oxford, United Kingdom

    3 publications · 67 citations

    3

    h-index

1–5 of 142

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 190 citations

    6

    h-index

  2. Figshare

    Repository · 2 publications · 415 citations

    2

    h-index

  3. IEEE Transactions on Dependable and Secure Computing

    TDSC · 2 publications · 77 citations

    2

    h-index

  4. 1

    h-index

  5. 1

    h-index

1–5