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\n  \n 2024\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Atropos: Effective Fuzzing of Web Applications for Server-Side Vulnerabilities.\n \n \n \n \n\n\n \n Güler, E.; Schumilo, S.; Schloegel, M.; Bars, N.; Görz, P.; Xu, X.; Kaygusuz, C.; and Holz, T.\n\n\n \n\n\n\n In Proceedings of the 33rd USENIX Security Symposium, 2024. \n \n\n\n\n
\n\n\n\n \n \n \"Atropos: paper\n  \n \n \n \"Atropos: github\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{atropos,\n  title={{Atropos: Effective Fuzzing of Web Applications for Server-Side Vulnerabilities}},\n  author={Emre G{\\"{u}}ler and Sergej Schumilo and Moritz Schloegel and Nils Bars and Philipp G{\\"{o}}rz and Xinyi Xu and Cemal Kaygusuz and Thorsten Holz},\n  booktitle={Proceedings of the 33rd USENIX Security Symposium},\n  year={2024},\n  url_paper = {https://www.usenix.org/system/files/sec23winter-prepub-167-guler.pdf},\n  url_github = {https://github.com/cispa-syssec/atropos-legacy}\n}\n\n
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\n  \n 2023\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Systematic assessment of fuzzers using mutation analysis.\n \n \n \n \n\n\n \n Görz, P.; Mathis, B.; Hassler, K.; Güler, E.; Holz, T.; Zeller, A.; and Gopinath, R.\n\n\n \n\n\n\n In Proceedings of the 32nd USENIX Security Symposium, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"Systematic paper\n  \n \n \n \"Systematic github\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{gorz2023systematic,\n  title={Systematic assessment of fuzzers using mutation analysis},\n  author={G{\\"o}rz, Philipp and Mathis, Bj{\\"o}rn and Hassler, Keno and G{\\"u}ler, Emre and Holz, Thorsten and Zeller, Andreas and Gopinath, Rahul},\n  booktitle={Proceedings of the 32nd USENIX Security Symposium},\n  year={2023},\n  url_paper = {https://www.usenix.org/system/files/usenixsecurity23-gorz.pdf},\n  url_github = {https://github.com/CISPA-SysSec/mua_fuzzer_bench}\n}\n\n
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\n  \n 2021\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n CollabFuzz: A Framework for Collaborative Fuzzing.\n \n \n \n \n\n\n \n Österlund, S.; Geretto, E.; Jemmett, A.; Güler, E.; Görz, P.; Holz, T.; Giuffrida, C.; and Bos, H.\n\n\n \n\n\n\n In European Workshop on Systems Security (EuroSec), 2021. \n \n\n\n\n
\n\n\n\n \n \n \"CollabFuzz: paper\n  \n \n \n \"CollabFuzz: github\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 444 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{collabfuzz,\n\ttitle = {{CollabFuzz}: {A} {Framework} for {Collaborative} {Fuzzing}},\n\turl_paper  = {https://download.vusec.net/papers/collabfuzz_eurosec21.pdf},\n\turl_github = {https://github.com/vusec/collabfuzz},\n\tbooktitle = {{European Workshop on Systems Security (EuroSec)}},\n\tauthor = {Österlund, Sebastian and Geretto, Elia and Jemmett, Andrea and Güler, Emre and Görz, Philipp and Holz, Thorsten and Giuffrida, Cristiano and Bos, Herbert},\n\tyear = {2021},\n\tkeywords = {class\\_testing, proj\\_react, proj\\_securecode, type\\_ast, type\\_workshop},\n}\n
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\n  \n 2020\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Cupid : Automatic Fuzzer Selection for Collaborative Fuzzing.\n \n \n \n \n\n\n \n Güler, E.; Görz, P.; Geretto, E.; Jemmett, A.; Österlund, S.; Bos, H.; Giuffrida, C.; and Holz, T.\n\n\n \n\n\n\n Annual Computer Security Applications Conference (ACSAC). 2020.\n \n\n\n\n
\n\n\n\n \n \n \"Cupid paper\n  \n \n \n \"Cupid github\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 69 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{cupid,\nabstract = {Combining the strengths of individual fuzzing methods is an appealing idea to find software faults more efficiently, especially when the computing budget is limited. In prior work, EnFuzz introduced the idea of ensemble fuzzing and devised three heuristics to classify properties of fuzzers in terms of diversity. Based on these heuristics, the authors manually picked a combination of different fuzzers that collaborate. In this paper, we generalize this idea by collecting and applying empirical data from single, isolated fuzzer runs to automatically identify a set of fuzzers that complement each other when executed collaboratively. To this end, we present Cupid, a collaborative fuzzing framework allowing automated, data-driven selection of multiple complementary fuzzers for parallelized and distributed fuzzing. We evaluate the automatically selected target-independent combination of fuzzers by Cupid on Google's fuzzer-test-suite, a collection of real-world binaries, as well as on the synthetic Lava-M dataset. We find that Cupid outperforms two expert-guided, target-specific and hand-picked combinations on Google's fuzzer-test-suite in terms of branch coverage, and improves bug finding on Lava-M by 10%. Most importantly, we improve the latency for obtaining 95% and 99% of the coverage by 90% and 64%, respectively. Furthermore , Cupid reduces the amount of CPU hours needed to find a high-performing combination of fuzzers by multiple orders of magnitude compared to an exhaustive evaluation.},\nauthor = {G{\\"{u}}ler, Emre and G{\\"{o}}rz, Philipp and Geretto, Elia and Jemmett, Andrea and {\\"{O}}sterlund, Sebastian and Bos, Herbert and Giuffrida, Cristiano and Holz, Thorsten},\njournal = {Annual Computer Security Applications Conference (ACSAC)},\ntitle = {{Cupid : Automatic Fuzzer Selection for Collaborative Fuzzing}},\nyear = {2020},\nurl_paper =    {https://www.ei.ruhr-uni-bochum.de/media/emma/veroeffentlichungen/2020/09/26/ACSAC20-Cupid_TiM9H07.pdf},\nurl_github =     {https://github.com/RUB-SysSec/cupid}\n}\n\n
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\n Combining the strengths of individual fuzzing methods is an appealing idea to find software faults more efficiently, especially when the computing budget is limited. In prior work, EnFuzz introduced the idea of ensemble fuzzing and devised three heuristics to classify properties of fuzzers in terms of diversity. Based on these heuristics, the authors manually picked a combination of different fuzzers that collaborate. In this paper, we generalize this idea by collecting and applying empirical data from single, isolated fuzzer runs to automatically identify a set of fuzzers that complement each other when executed collaboratively. To this end, we present Cupid, a collaborative fuzzing framework allowing automated, data-driven selection of multiple complementary fuzzers for parallelized and distributed fuzzing. We evaluate the automatically selected target-independent combination of fuzzers by Cupid on Google's fuzzer-test-suite, a collection of real-world binaries, as well as on the synthetic Lava-M dataset. We find that Cupid outperforms two expert-guided, target-specific and hand-picked combinations on Google's fuzzer-test-suite in terms of branch coverage, and improves bug finding on Lava-M by 10%. Most importantly, we improve the latency for obtaining 95% and 99% of the coverage by 90% and 64%, respectively. Furthermore , Cupid reduces the amount of CPU hours needed to find a high-performing combination of fuzzers by multiple orders of magnitude compared to an exhaustive evaluation.\n
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\n  \n 2019\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n AntiFuzz: Impeding Fuzzing Audits of Binary Executables.\n \n \n \n \n\n\n \n Güler, E.; Aschermann, C.; Abbasi, A.; and Holz, T.\n\n\n \n\n\n\n Proceedings of the 28th USENIX Security Symposium,1931–1947. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"AntiFuzz: paper\n  \n \n \n \"AntiFuzz: github\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 196 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{antifuzz,\nabstract = {A general defense strategy in computer security is to increase the cost of successful attacks in both computational resources as well as human time. In the area of binary security, this is commonly done by using obfuscation methods to hinder reverse engineering and the search for software vulnerabilities. However, recent trends in automated bug finding changed the modus operandi. Nowadays it is very common for bugs to be found by various fuzzing tools. Due to ever-increasing amounts of automation and research on better fuzzing strategies, large-scale, dragnet-style fuzzing of many hundreds of targets becomes viable. As we show, current obfuscation techniques are aimed at increasing the cost of human understanding and do little to slow down fuzzing. In this paper, we introduce several techniques to protect a binary executable against an analysis with automated bug finding approaches that are based on fuzzing, symbolic/concolic execution, and taint-assisted fuzzing (commonly known as hybrid fuzzing). More specifically, we perform a systematic analysis of the fundamental assumptions of bug finding tools and develop general countermeasures for each assumption. Note that these techniques are not designed to target specific implementations of fuzzing tools, but address general assumptions that bug finding tools necessarily depend on. Our evaluation demonstrates that these techniques effectively impede fuzzing audits, while introducing a negligible performance overhead. Just as obfuscation techniques increase the amount of human labor needed to find a vulnerability, our techniques render automated fuzzing-based approaches futile.},\nauthor = {G{\\"{u}}ler, Emre and Aschermann, Cornelius and Abbasi, Ali and Holz, Thorsten},\nisbn = {9781939133069},\njournal = {Proceedings of the 28th USENIX Security Symposium},\npages = {1931--1947},\ntitle = {{AntiFuzz: Impeding Fuzzing Audits of Binary Executables}},\nyear = {2019},\nurl_paper =    {https://www.usenix.org/system/files/sec19-guler.pdf},\nurl_github =     {https://github.com/RUB-SysSec/antifuzz}\n}\n\n
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\n A general defense strategy in computer security is to increase the cost of successful attacks in both computational resources as well as human time. In the area of binary security, this is commonly done by using obfuscation methods to hinder reverse engineering and the search for software vulnerabilities. However, recent trends in automated bug finding changed the modus operandi. Nowadays it is very common for bugs to be found by various fuzzing tools. Due to ever-increasing amounts of automation and research on better fuzzing strategies, large-scale, dragnet-style fuzzing of many hundreds of targets becomes viable. As we show, current obfuscation techniques are aimed at increasing the cost of human understanding and do little to slow down fuzzing. In this paper, we introduce several techniques to protect a binary executable against an analysis with automated bug finding approaches that are based on fuzzing, symbolic/concolic execution, and taint-assisted fuzzing (commonly known as hybrid fuzzing). More specifically, we perform a systematic analysis of the fundamental assumptions of bug finding tools and develop general countermeasures for each assumption. Note that these techniques are not designed to target specific implementations of fuzzing tools, but address general assumptions that bug finding tools necessarily depend on. Our evaluation demonstrates that these techniques effectively impede fuzzing audits, while introducing a negligible performance overhead. Just as obfuscation techniques increase the amount of human labor needed to find a vulnerability, our techniques render automated fuzzing-based approaches futile.\n
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