Distinguished Paper at NDSS 2026: Fuzzilicon finds hidden flaws inside real x86 CPUs

CPU bugs are usually hunted in simulation, before the chip is made. Fuzzilicon tests the finished chip itself, by instrumenting its microcode, and found vulnerabilities that had never been reported.

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Our paper, "Fuzzilicon: A Post-Silicon Microcode-Guided x86 CPU Fuzzer," received a Distinguished Paper Award at NDSS Symposium 2026, one of the top venues in network and systems security. The work is co-authored by Sentinel Intelligence co-founder Lichao Wu, together with Johannes Lenzen, Mohamadreza Rostami, and Ahmad-Reza Sadeghi (Technical University of Darmstadt).

What Fuzzilicon does

Fuzzing feeds a system huge numbers of generated inputs and watches for unexpected behavior. For CPUs, that has mostly meant fuzzing a design in simulation, which needs access to the chip's source design and can miss what happens on real silicon. Fuzzilicon is the first post-silicon fuzzing framework for real-world x86 CPUs. It uses reverse-engineered access to Intel's microcode update interface to instrument the microcode, getting feedback from inside the processor while it runs.

  • Works on the finished chip, with no access to its design files and no vendor support.
  • Applied to Intel's Goldmont microarchitecture, it reported five findings, including two previously unknown microcode-level speculative-execution vulnerabilities.
  • It automatically rediscovered the µSpectre class of vulnerabilities, which earlier work had found by hand.
  • Up to 31× lower coverage-collection overhead than baseline techniques.

The rediscovery result is the one worth sitting with. A class of bugs that once took manual expert analysis to find now falls out of an automated, coverage-guided search.

Our take

The systems we depend on most are often ones we can't see inside, whether that's a processor or a commercial AI model. Fuzzilicon shows that rigorous, automated testing is still possible from the outside, without the vendor's cooperation. That's the same principle behind independent verification of AI agents: you shouldn't have to take a system's safety on trust.

Want to talk to the research team?

We're always happy to discuss the work behind Sentinel, or how it applies to your AI agents.