CyberIntel ⬡ News
★ Saved ◆ Cyber Reads
← Back ◬ AI & Machine Learning Mar 23, 2026

From Precise to Random: A Systematic Differential Fault Analysis of the Lightweight Block Cipher Lilliput

arXiv Security Archived Mar 23, 2026 ✓ Full text saved

arXiv:2603.19781v1 Announce Type: new Abstract: At SAC 2013, Berger et al. first proposed the Extended Generalized Feistel Networks (EGFN) structure for the design of block ciphers with efficient diffusion. Later, based on the Type-2 EGFN, they instantiated a new lightweight block cipher named Lilliput (published in IEEE Transactions on Computers, Vol. 65, Issue 7, 2016). According to published cryptanalysis results, Lilliput is sufficiently secure against theoretical attacks such as differentia

Full text archived locally
✦ AI Summary · Claude Sonnet


    Computer Science > Cryptography and Security [Submitted on 20 Mar 2026] From Precise to Random: A Systematic Differential Fault Analysis of the Lightweight Block Cipher Lilliput Peipei Xie, Siwei Chen, Zejun Xiang, Shasha Zhang, Xiangyong Zeng At SAC 2013, Berger et al. first proposed the Extended Generalized Feistel Networks (EGFN) structure for the design of block ciphers with efficient diffusion. Later, based on the Type-2 EGFN, they instantiated a new lightweight block cipher named Lilliput (published in IEEE Transactions on Computers, Vol. 65, Issue 7, 2016). According to published cryptanalysis results, Lilliput is sufficiently secure against theoretical attacks such as differential, linear, boomerang, and integral attacks, which rely on the statistical properties of plaintext and ciphertext. However, there is a lack of analysis regarding its resistance to physical attacks in real-world scenarios, such as fault attacks. In this paper, we present the first systematic differential fault analysis (DFA) of Lilliput under three nibble-oriented fault models with progressively relaxed adversarial assumptions to comprehensively assess its fault resilience. In Model I (multi-round fixed-location), precise fault injections at specific rounds recover the master key with a 98% success rate using only 8 faults. Model II (single-round fixed-location) relaxes the multi-round requirement, demonstrating that 8 faults confined to a single round are still sufficient to achieve a 99% success rate by exploiting Lilliput's diffusion properties and DDT-based constraints. Model III (single-round random-location) further weakens the assumption by allowing faults to occur randomly among the eight rightmost branches of round 27. By uniquely identifying the fault location from ciphertext differences with high probability, the attack remains highly feasible, achieving over 99% success with 33 faults and exceeding 99.5% with 36 faults. Our findings reveal a significant vulnerability of Lilliput to practical fault attacks across different adversary capabilities in real-world scenarios, providing crucial insights for its secure implementation. Comments: 37 pages, 19 figures Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.19781 [cs.CR]   (or arXiv:2603.19781v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.19781 Focus to learn more Submission history From: Peipei Xie [view email] [v1] Fri, 20 Mar 2026 09:17:28 UTC (3,958 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 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?)
    💬 Team Notes
    Article Info
    Source
    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Mar 23, 2026
    Archived
    Mar 23, 2026
    Full Text
    ✓ Saved locally
    Open Original ↗