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Update on Overleaf.

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nb72soza Bittner
2025-07-07 00:56:57 +00:00
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Chapters/Implementation.tex
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@@ -60,7 +60,7 @@ While our approach allows for a more precise control, it has some drawbacks. \gl
\paragraph{Fuzzing Strategy}
When applying mutations to \gls{apdu} messages, we encountered a common issue: random mutations frequently produce invalid \gls{asn1} structures. This narrows the testing focus to the \gls{asn1} decoder, which represents only a small portion of the total \gls{euicc} logic. Despite this limitation, fuzzing at the decoding layer can still yield valuable results, as parsing flaws in \gls{asn1}-based decoders have historically led to critical vulnerabilities~\cite{mitre_cve_2003, nist_nvd_2024, nist_nvd_2025}.
When applying mutations to \gls{apdu} messages, we encountered a common issue: random mutations frequently produce invalid \gls{asn1} structures. This narrows the testing focus to the \gls{asn1} decoder, which represents only a small part of the total \gls{euicc} logic. Still, fuzzing at the decoding layer can still yield valuable results, as parsing flaws in \gls{asn1}-based decoders have historically led to critical vulnerabilities~\cite{mitre_cve_2003, nist_nvd_2024, nist_nvd_2025}.
To improve the depth and scope of our fuzzing efforts, we adapted our implementation to generate and mutate structurally valid input instead. By preserving the syntactic and semantic correctness of \gls{asn1} structures, we enabled the fuzzer to exercise deeper layers of application logic. This allowed us to test state transitions, logical constraints, and error handling mechanisms that would otherwise remain untriggered by malformed data.