LAVA: Large-Scale Automated Vulnerability Addition
By Brendan Dolan-Gavitt, P. Hulin, Engin Kirda, T. Leek, Andrea Mambretti, William K. Robertson, F. Ulrich, Ryan Whelan. IEEE Symposium on Security and Privacy, 2016
365 citations recorded by the source corpus.
Subfields: Software, Signal Processing
View Paper: https://www.semanticscholar.org/paper/47f411eda6b7f5dd623127b39a6b8fef0f4af523
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Software
- Michael Pradel
- Yang Liu
- Bihuan Chen
- Thorsten Holz
- Cornelius Aschermann
- Yan Shoshitaishvili
- Mathias Payer
- Hui Peng
In Signal Processing
- Thorsten Holz
- Bihuan Chen
- Michael Pradel
- Mathias Payer
- Cornelius Aschermann
- Yan Shoshitaishvili
- Hui Peng
- Peng Chen
Citations in the corpus
- Steelix: program-state based binary fuzzing (2017, methodology): “The LAVA authors have demonstrated that a coverage-based fuzzer (FUZZER) and a SAT-based approach (SES) cannot find the injected bugs effectively [27].”
- DeepBugs: a learning approach to name-based bug detection (2018, background): “ from version histories [Brown et al. 2017], could be an interesting way to extend DeepBugs with further bug detectors. To evaluate bug detection tools, automated bug seeding has been proposed. LAVA [Dolan-Gavitt et al. 2016] and EvilCoder [Pewny and Holz 2016] introduce artificial security bugs int”
- Superion: Grammar-Aware Greybox Fuzzing (2018, neutral): “First, we did not evaluate Superion on standardized data sets, e.g., LAVA [ 21] and CGC [2].”
- QSYM : A Practical Concolic Execution Engine Tailored for Hybrid Fuzzing (2018, methodology): “We ran QSYM with and without the optimistic solving on the LAVA-M dataset for five hours, which is the test duration set by the original LAVA work [10].”
- VUzzer: Application-aware Evolutionary Fuzzing (2017, methodology): “To expose VUzzer to a variety of applications, we chose to test VUzzer on three different datasets A. DARPA CGC binaries [15], B. miscellaneous applications with binary format as used in [43], and C. a set of buggy binaries recently generated by LAVA [17].”
- Angora: Efficient Fuzzing by Principled Search (2018, methodology): “Table 1 compares the bugs found by Angora with other fuzzers on the LAVA-M data set [9].”
- T-Fuzz: Fuzzing by Program Transformation (2018, methodology): “The LAVA dataset contains a set of vulnerable programs created by automatically injecting bugs using the technique proposed in [24].”
- Evaluating Fuzz Testing (2018, methodology): “The latter comprises CGC (“C”) [14], LAVA-M (“L”) [16], and hand-selected programs with synthetically injected bugs (“S”).”
- CollAFL: Path Sensitive Fuzzing (2018, methodology): “Furthermore, we also evaluated on the LAVA-M dataset [14], which has 4 applications instrumented with crafted vulnerabilities.”
- REDQUEEN: Fuzzing with Input-to-State Correspondence (2019, methodology): “We only missed two of the 2265 listed vulnerabilities in the LAVA-M data set (>99.9% bug coverage).”