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PowerFuzz: Power-Based Black-Box Firmware Fuzzing

arXiv Security Archived Jun 24, 2026 ✓ Full text saved

arXiv:2606.24692v1 Announce Type: new Abstract: Fuzzing is widely used for software and hardware verification, offering an effective alternative to random testing. While gray-box fuzzers benefit from full visibility into the system under test and can leverage execution feedback such as branch coverage, these approaches are not applicable when verifying systems whose firmware or binaries are not publicly available. In such scenarios, obtaining coverage information for guiding the fuzzer becomes i

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    Computer Science > Cryptography and Security [Submitted on 23 Jun 2026] PowerFuzz: Power-Based Black-Box Firmware Fuzzing Dakshina Tharindu, Sahan Sanjaya, Philip Baptist, Prabhat Mishra Fuzzing is widely used for software and hardware verification, offering an effective alternative to random testing. While gray-box fuzzers benefit from full visibility into the system under test and can leverage execution feedback such as branch coverage, these approaches are not applicable when verifying systems whose firmware or binaries are not publicly available. In such scenarios, obtaining coverage information for guiding the fuzzer becomes infeasible. In this paper, we introduce PowerFuzz, a statistical black-box fuzzing framework that leverages power side-channel measurements as a substitute for binary instrumentation, requiring no internal visibility into the target firmware. A central challenge in black-box firmware fuzzing is determining the executed branches during test execution. To address this challenge, we use power traces to identify branches utilizing a sliding window followed by a growing window full-trace correlation method. This approach also enables the construction of a high-level control-flow graph of the black-box firmware, which we utilize to drive the fuzzer to unexplored execution paths. Extensive evaluation using three embedded hardware platforms and ten firmware benchmarks demonstrates that PowerFuzz can provide branch coverage comparable (within 13.5%) to gray-box fuzzers while significantly outperforming (up to 22%) state-of-the-art black-box fuzzers. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.24692 [cs.CR]   (or arXiv:2606.24692v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.24692 Focus to learn more Submission history From: Dakshina Tharindu [view email] [v1] Tue, 23 Jun 2026 15:21:40 UTC (3,371 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 24, 2026
    Archived
    Jun 24, 2026
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