Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2001.00806

Verifying Cryptographic Security Implementations in C Using Automated Model Extraction

Mihhail Aizatulin
View original source ↗Read source document ↗Export metadata ↓

Abstract

This thesis presents an automated method for verifying security properties of protocol implementations written in the C language. We assume that each successful run of a protocol follows the same path through the C code, justified by the fact that typical security protocols have linear structure. We then perform symbolic execution of that path to extract a model expressed in a process calculus similar to the one used by the CryptoVerif tool. The symbolic execution uses a novel algorithm that allows symbolic variables to represent bitstrings of potentially unknown length to model incoming protocol messages. The extracted models do not use pointer-addressed memory, but they may still contain low-level details concerning message formats. In the next step we replace the message formatting expressions by abstract tupling and projection operators. The properties of these operators, such as the projection operation being the inverse of the tupling operation, are typically only satisfied with respect to inputs of correct types. Therefore we typecheck the model to ensure that all type-safety constraints are satisfied. The resulting model can then be verified with CryptoVerif to obtain a computational security result directly, or with ProVerif, to obtain a computational security result by invoking a computational soundness theorem. Our method achieves high automation and does not require user input beyond what is necessary to specify the properties of the cryptographic primitives and the desired security goals. We evaluated the method on several protocol implementations, totalling over 3000 lines of code. The biggest case study was a 1000-line implementation that was independently written without verification in mind. We found several flaws that were acknowledged and fixed by the authors, and were able to verify the fixed code without any further modifications to it.

Explore related subjects

cs.CRcs.PL

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mihhail Aizatulin. 2020-01-03. Verifying Cryptographic Security Implementations in C Using Automated Model Extraction. https://arxiv.org/abs/2001.00806

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

KEEP EXPLORING

Related papers

arXiv2026

Starfish: Rebalancing Multi-Party Off-Chain Payment Channels

Blockchain technology has revolutionized the way transactions are executed, but scalability remains a major challenge. Payment Channel Network (PCN), as a Layer-2 scaling solution, has been proposed to address this issue. However, skewed payments can deplete the balance of one party within a channel, restricting the ability of PCNs to transact through a path and subsequently reducing the transaction success rate. To address this issue, the technology of rebalancing has been proposed. However, existing rebalancing strategies in PCNs are limited in their efficiency and applicability under realistic network topologies. Cycle-based approaches only enable rebalancing among nodes that form cycles, while non-cycle-based approaches incur high on-chain operation costs. In this study, we propose Starfish, a rebalancing approach designed for the locally star-like structures formed by high-degree routing hubs and their incident channels in real-world PCNs. By leveraging a hub-centric structure, Starfish reduces the on-chain overhead of rebalancing from quadratic to linear in the number of channels, achieving high rebalancing efficiency. We formally prove the security of Starfish and conduct comparative experiments against existing rebalancing techniques to demonstrate its effectiveness.

cs.CR↗
arXiv2026

Proof-of-Authorship for Diffusion-based AI Generated Content

Recent advancements in AI-generated content (AIGC) have introduced new challenges in intellectual property protection and the authentication of generated objects. We focus on scenarios in which an author seeks to assert authorship of an object generated using latent diffusion models (LDMs), in the presence of adversaries who attempt to falsely claim authorship of objects they did not create. While proof-of-ownership has been studied in the context of multimedia content through techniques such as time-stamping and watermarking, these approaches face notable limitations. In contrast to traditional content creation sources (e.g., cameras), the LDM generation process offers greater control to the author. Specifically, the random seed used during generation can be deliberately chosen. By binding the seed to the author's identity using cryptographic functions, the author can assert to be the creator of the object. We refer to this stronger guarantee as proof-of-authorship, since only the creator of the object can legitimately claim the object. This contrasts with proof-of-ownership via time-stamping or watermarking, where any entity could potentially claim ownership of an object by being the first to timestamp or embed the watermark. We propose a proof-of-authorship framework involving a probabilistic adjudicator who quantifies the probability that a claim is false. Furthermore, unlike prior approaches, the proposed framework does not involve any secret. We explore various attack scenarios and analyze design choices using Stable Diffusion 2.1 and XL as representative case studies.

cs.CR↗
arXiv2026

Impact Analysis of Speech Representation Learning Models for Acoustic Side-Channel Attack

Acoustic side-channel attacks (ASCA) on keyboards have gained increasing attention, yet impact of speech representation learning models in ASCA remains unexplored. Addressing this, we introduce KEYAC, a dataset designed to analyze representation generalization for ASCA under both standard and VoIP codec settings. On KEYAC, we evaluate six representation learning models under zero-shot and partial fine-tuning settings using fully connected and convolutional networks. Results show that while partial fine-tuning improves performance, models struggle to generalize across VoIP codecs. We hypothesize this limitation stems from inadequate modeling of nonlinear feature interactions in conventional fine-tuning architectures. To address this, we employ Kolmogorov-Arnold Networks (KAN) for fine-tuning. Empirical results show that KAN-based fine-tuning consistently outperforms the baselines and establishes a new state-of-the-art on KEYAC.

cs.CR↗