Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2607.08865

Entropy Bootstrapping for Wireless Embedded Systems

Javier Blanco-Romero·Florina Almenares Mendoza·Daniel Díaz-Sánchez·Andrés Marín-López
View original source ↗Read source document ↗Export metadata ↓

Abstract

Cryptographic protocols require unpredictable randomness at first boot, yet wireless sensors often wake with uninitialized hardware or noise sources running in unverified operating states. In platforms like the ESP32, statistical testing cannot catch this cold-start failure because the internal random number generator continues returning statistically plausible bytes even when analog radio noise is disabled, producing pure pseudorandomness by design. To prevent nodes from silently anchoring security in a single unverified source, we introduce a defense-in-depth boot architecture governed by explicit source-state admission. The system combines three orthogonal roots of unpredictability: startup noise from uninitialized SRAM cells, local wireless RNG output gathered during a bounded window defined by an external packet burst, and a signed post-quantum asymmetric capsule delivering fresh entropy from a trusted peer without requiring local random generation. Rather than blending unverified numbers, the admission policy gates entropy credit on the verified operating mode of the hardware, admitting wireless RNG output only while the radio frequency circuits are active. Empirical trials confirm that incoming network traffic successfully bounds repeatable, uncorrelated hardware sampling windows, while remote capsule verification completes with minimal processing overhead. By coupling physical operating state to cryptographic accounting, commodity microcontrollers can bootstrap reliable seed entropy across complementary roots without relying on additional dedicated security hardware.

Explore related subjects

cs.CR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Javier Blanco-Romero, Florina Almenares Mendoza, Daniel Díaz-Sánchez, Andrés Marín-López. 2026-09-06. Entropy Bootstrapping for Wireless Embedded Systems. https://arxiv.org/abs/2607.08865

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

cs.CR subject

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

arXiv2026

Attack Tree Distance: a practical examination of tree difference measurement within cyber security

Attack trees are a popular threat modeling method. In practice, there is often a need to compare attack tree models produced by human experts, based on both the structure of the tree and the meaning of the node labels. In this work, we investigate the problem of comparing attack trees and measuring their similarity. We define five different measures for measuring the distance between two attack trees: Label Distance (LD), Tree Edit Distance (TED), Radical Distance (RD), Multiset Distance (MSD) and Weighted Sum Distance (WSD). We further propose a repeatable method of both theoretical and experimental attack tree distance measures validation. Our theoretical validation consists of a series of basic transformations to evaluate the behavior of distance measures with respect to specific types of transformations that may appear between two attack trees. To experimentally validate our distance measures, we designed and executed a human study ($n=39$) to collect a dataset of attack trees to be used for evaluation and comparison of the measures. From our theoretical and experimental results, we find that applying semantic similarity as a means of comparing node labels is a valid approach. Further, we find four of the five attack tree distance measures are valid approaches in certain, varying circumstances. Our results suggest that these methods can already be used to identify similar real-world attack trees. Overall, this work lays the groundwork for improved threat model analysis, validation of AI-generated attack trees, and future research into threat similarity measurement in cybersecurity.

cs.CR↗
arXiv2026

Probabilistic Modeling of Jailbreak on Multimodal LLMs: From Quantification to Application

Recently, Multimodal Large Language Models (MLLMs) have demonstrated their superior ability in understanding multimodal content. However, they remain vulnerable to jailbreak attacks, which exploit weaknesses in their safety alignment to generate harmful responses. Previous studies categorize jailbreaks as successful or failed based on whether responses contain malicious content. However, given the stochastic nature of MLLM responses, this binary classification of an input's ability to jailbreak MLLMs is inappropriate. Derived from this viewpoint, we introduce jailbreak probability to quantify the jailbreak potential of an input, which represents the likelihood that MLLMs generated a malicious response when prompted with this input. We approximate this probability through multiple queries to MLLMs. After modeling the relationship between input hidden states and their corresponding jailbreak probability using Jailbreak Probability Prediction Network (JPPN), we use continuous jailbreak probability for optimization. Specifically, we propose Jailbreak-Probability-based Attack (JPA) that optimizes adversarial perturbations on input image to maximize jailbreak probability, and further enhance it as Multimodal JPA (MJPA) by including monotonic text rephrasing. To counteract attacks, we also propose Jailbreak-Probability-based Finetuning (JPF), which minimizes jailbreak probability through MLLM parameter updates. Extensive experiments show that (1) (M)JPA yields significant improvements when attacking a wide range of models under both white and black box settings. (2) JPF vastly reduces jailbreaks by at most over 60\%. Both of the above results demonstrate the significance of introducing jailbreak probability to make nuanced distinctions among input jailbreak abilities.

cs.CR↗
arXiv2026

Differential Fault Analysis of Lilliput under Random-Location Nibble Faults

Differential fault analysis (DFA) is an important technique for evaluating the implementation-level security of block ciphers. Many DFA attacks assume that the adversary can inject faults into a selected internal word, nibble, or branch. Such fixed-location assumptions are convenient for deriving key-recovery equations, but they may overestimate the adversary's spatial control and may obscure the branch-dependent leakage behavior of multi-branch structures. In this paper, we study the lightweight block cipher Lilliput under a fixed-timing full-branch random-location nibble fault model. The attacker is assumed to induce a nonzero nibble fault in round 27, while the affected branch is randomly distributed over all sixteen state branches and is unknown to the attacker. The main challenge is to convert faulty ciphertexts with unknown injection locations into usable key-recovery constraints. We analyze the fault propagation induced by the EGFN structure of Lilliput and derive ciphertext-difference conditions for identifying the injected branch. The proposed branch-identification approach has a DDT-based combinatorial estimate of at least 99.9909% and achieves 99.9983% accuracy in 2^{20} random fault simulations. Once the fault branch is determined, we classify the corresponding propagation patterns according to whether the injected fault value and the intermediate S-box output difference can be uniquely determined. For each case, we derive DDT-based constraints on the last-round and penultimate-round subkeys and combine multiple faulty ciphertexts by candidate-set intersection. Simulation experiments over 2^{15} trials show that the attack reaches key-recovery success rates of over 90%, 95%, and 99% with 32, 36, and 46 faulty ciphertexts, respectively. These results show that Lilliput exhibits exploitable branch-dependent leakage even when the attacker cannot control the exact fault location.

cs.CR↗