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Some empirical support for the conjecture: An exponential increase in cores gives a linear increase in *percentage coverage improvement*. LibFuzzer on FTS, 14 runs x 11 subjects x 6h. 1 core =45min, 8 cores=360min campaign. 100% improvement = 2x more features. https://twitter.com/mboehme_/status/1220536318989438976 …pic.twitter.com/C2iLN72gJe
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Marcel Böhme proslijedio/la je Tweet
HWASAN (think of it as ASAN v2) has become available to developers on Android outside of Google. If you use C or C++ on Android, please give it a try. https://developer.android.com/ndk/guides/hwasan … HWASAN is also available on Aarch64 Linux with a recent kernel.
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Marcel Böhme proslijedio/la je Tweet
Paper Review for CacheOut is now up on YouTube! It's 2 parts totaling 11 hours, have fun!https://www.youtube.com/watch?v=jgLnVoh9Q8I&list=PLSkhUfcCXvqGjCp6SNUyuZ4UUKezk9pwF …
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Marcel Böhme proslijedio/la je Tweet
Ich habe 2 offene Stellen für Wissenschaftliche Mitarbeiter*innen am
@CISPA zu besetzen. Vollzeit, langer Vertrag, viel Zeit für eigene Forschung, in einem wunderbaren internationalen Team. Freue mich über jeden RT und noch mehr über Bewerbungen!https://jobs.cispa.saarland/de_DE/jobs/detail/wissenschaftlicher-mitarbeiter-m-w-d-als-doktorand-m-w-d-in-vollzeit-34 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Marcel Böhme proslijedio/la je Tweet
Paper Review S01E02 - CacheOut! EU friendly steam, starting in 15 min!https://www.twitch.tv/gamozo
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Marcel Böhme proslijedio/la je Tweet
Check out
@Nosoynadiemas ' tips on Fuzzing, to overcome known challenges and maximize results:https://securitylab.github.com/research/fuzzing-challenges-solutions-1 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Marcel Böhme proslijedio/la je Tweet
ToB + GitHub webinar, iVerify update, “Whose Curve is it Anyway?”
- https://mailchi.mp/trailofbits/january-2019 …pic.twitter.com/AB53OM7wGu
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Marcel Böhme proslijedio/la je Tweet
Android testing tools such as Monkey are very fast, and human testers are smart, memorizing what has been tested and keeping finding interesting screens to test. What if these tools have such human intelligence? You will find it in our ICSE'20 work: https://www.comp.nus.edu.sg/~dongz/res/time-travel-testing-21-01-2020.pdf …pic.twitter.com/3bi4yvmXYj
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Marcel Böhme proslijedio/la je Tweet
Thrilled to see that our fuzzing efforts are now also described at https://www.sqlite.org/testing.html .pic.twitter.com/gHpL6BwhNU
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For LibFuzzer on FTS, an 𝗲𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 increase in # cores gives a 𝗹𝗶𝗻𝗲𝗮𝗿 increase in # features covered that are not covered by a same-length single-core campaign. Expecting the same for # bugs. Thoughts? Linking here for completeness.https://twitter.com/mboehme_/status/1220908919079358466 …
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Marcel Böhme proslijedio/la je Tweet
Check out the RLCheck preprint! Cool idea to try and use reinforcement learning to tune generators to generate more valid (assumption-satisfying) inputs. With
@SameerReddy0@moarbugs@koushik77 https://www.carolemieux.com/rlcheck_preprint.pdf …pic.twitter.com/zm6THQXS6x
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So, we definitely need some experiments to look at the empirical distribution of bugs / coverage units to test the conjecture.
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Now let's suppose we want to cover basic blocks (BBs). For the most "difficult" BB (lowest θ_i), the same reasoning applies. However, most BBs (that the fuzzer *can* cover) seem "easy" to cover (high θ_i relative to time budget). For those, adding 10x cores doesn't do much.
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So, for a *single bug*, if the prob. θ that a random input finds it is really low compared to the available time budget, adding an order of magnitude more cores should also improve the chances of finding it by about an order of magnitude. What about coverage (or multiple bugs)?
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If you do find this interesting, I have open PhD positions: https://mboehme.github.io/phds
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Follow the thread for some maths, but the lesson is that blackbox fuzzers *scale* extremely well for all practical purposes, at least in terms of finding difficult-to-find bugs! Hope this raises some interest for the theory of fuzzing efficiency :)https://twitter.com/mboehme_/status/1220536318989438976 …
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For θ=10^-8, let's give one fuzzer 10x cores and vary the time budget n instead. The prob. to discover the bug is 10x higher for n<10^5 when it starts dropping to 1x at about 10^8. Now, this should be no surprise anymore :)pic.twitter.com/aPHo1SDalZ
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Now it gets REALLY interesting. If the probability θ is *really* low relative to the available time budget (i.e., the practical case), putting in 10x more resources makes perfect sense --- but only up until a certain point when factor improvement plateaus. For θ=10^-8 and n=10^3,pic.twitter.com/OkSlVkLCIz
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Alright. Let the prob. that a random input exposes a bug be θ, then the exp. prob. that the bug is revealed after n inputs is 1-(1-θ)^n and 1-(1-θ)^(nx), resp. for a fuzzer with with x more cores. The factor improvement is ((1-θ)^n)-(1-θ)^(nx))/(1-θ)^n). For θ=10^-4 and n=10^3,pic.twitter.com/uboLmqz7L1
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Corollary: An exponential increase in the time budget available to your favourite fuzzer yields a linear increase in coverage achieved (or # bugs found) given a fixed # cores. Maybe less. Not entirely unrelated: https://mboehme.github.io/paper/TSE15.pdf
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