kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels
By Sergej Schumilo, Cornelius Aschermann, Robert Gawlik, Sebastian Schinzel, Thorsten Holz. USENIX Security Symposium, 2017
343 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/30023acba3ac198a7d260228dc51fda8414b8860
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Xiaogang Zhu
- Sheng Wen
- Andrea Fioraldi
- Cornelius Aschermann
- Seyit Ahmet Camtepe
- Dominik Christian Maier
- Yang Xiang
- Shuitao Gan
Citations in the corpus
- CollAFL: Path Sensitive Fuzzing (2018, methodology): “Syzkaller [5] and kAFL [31] utilize QEMU as well as hardware features (e.”
- REDQUEEN: Fuzzing with Input-to-State Correspondence (2019, methodology): “We based our implementation of REDQUEEN on our fuzzer KAFL [37].”
- Evaluating Fuzz Testing (2018, background): “Other work focuses on increasing the speed of eval by making changes to the operating system [56] or using different low level primitives to observe the effect of executions [23, 25, 47].”
- Superion: Grammar-Aware Greybox Fuzzing (2018, unknown): “Besides, we excluded the non-deterministic code in WebKit and ChakraCore from instrumentation, following the technique in kAFL [58].”
- Fuzzing: A Survey for Roadmap (2022, background): “, QEMU or KVM) to run a target kernel [143, 164].”
- NAUTILUS: Fishing for Deep Bugs with Grammars (2019, methodology): “While there are several general-purpose generation-based fuzzers [10], [41], [48], many approaches directly target a specific use case: CSmith [51] for C, LangFuzz [36] and IFuzzer [47] for JavaScript interpreters, and many more [7], [14], [20], [28], [44], [45].”
- The Art, Science, and Engineering of Fuzzing: A Survey (2018, unknown): “FairFuzz [141] H# ✓ ✓ ✓† H#† ✓ ✓ ✓ go-fuzz [225] H# ✓ ✓ ✓ ✓ ✓ ✓ H# ✓ Hawkeye [56] H# ✓ ✓ ✓ ✓ H# ✓ ✓ honggfuzz [213] H# ✓ ✓ ✓ kAFL [191] H# ✓ ✓ LibFuzzer [9] H# ✓ ✓ ✓ ✓ ✓ ✓ MagicFuzzer [50] H# ✓ ✓ ✓ Nautilus [25] H# ✓ ✓ ✓ H# ✓ ✓ RaceFuzzer [197] H# ✓ ✓ ✓ RedQueen [26] H# ✓ ✓ H# ✓”
- AFL++ : Combining Incremental Steps of Fuzzing Research (2020, methodology): “Recently, R ED Q UEEN [5], based on KAFL [36], explored the possibility to bypass hard comparisons and checksum checks, like other previous works in literature [35] [12] [33] [44], but without the use of expensive techniques like taint tracking [46] or symbolic execution [6, 37].”