Robust Constant-Time Cryptography

Matthew Kolosick; Basavesh Ammanaghatta Shivakumar; Sunjay Cauligi; Marco Patrignani; Marco Vassena; Ranjit Jhala; Deian Stefan

arXiv · 2311.05831

Robust Constant-Time Cryptography

Matthew Kolosick·Basavesh Ammanaghatta Shivakumar·Sunjay Cauligi·Marco Patrignani·Marco Vassena·Ranjit Jhala·Deian Stefan

View original source ↗Read source document ↗Export metadata ↓

Abstract

The constant-time property is considered the security standard for cryptographic code. Code following the constant-time discipline is free from secret-dependent branches and memory accesses, and thus avoids leaking secrets through cache and timing side-channels. The constant-time property makes a number of implicit assumptions that are fundamentally at odds with the reality of cryptographic code. Constant-time is not robust. The first issue with constant-time is that it is a whole-program property: It relies on the entirety of the code base being constant-time. But, cryptographic developers do not generally write whole programs; rather, they provide libraries and specific algorithms for other application developers to use. As such, developers of security libraries must maintain their security guarantees even when their code is operating within (potentially untrusted) application contexts. Constant-time requires memory safety. The whole-program nature of constant-time also leads to a second issue: constant-time requires memory safety of all the running code. Any memory safety bugs, whether in the library or the application, will wend their way back to side-channel leaks of secrets if not direct disclosure. And although cryptographic libraries should (and are) written to be memory-safe, it is unfortunately unrealistic to expect the same from every application that uses each library. We formalize robust constant-time and build a RobustIsoCrypt compiler that transforms the library code and protects the secrets even when they are linked with untrusted code. Our evaluation with SUPERCOP benchmarking framework shows that the performance overhead is less than five percent on average.

Explore related subjects

cs.CR

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Matthew Kolosick, Basavesh Ammanaghatta Shivakumar, Sunjay Cauligi, Marco Patrignani, Marco Vassena, Ranjit Jhala, Deian Stefan. 2023-11-10. Robust Constant-Time Cryptography. https://arxiv.org/abs/2311.05831

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

KEEP EXPLORING

Related papers

arXiv2026

Empirical Evaluation of Membership Inference Attacks on NLP Text Classifiers: A Baseline Study on SST-2

Membership inference attacks (MIAs) try to determine whether a specific record was used to train a model, a privacy risk that matters in natural language processing (NLP), where training data can contain sensitive user text. This paper presents a controlled benchmark of membership inference vulnerability for text classification on the GLUE SST-2 sentiment dataset. A TF-IDF + Logistic Regression pipeline and a fine-tuned DistilBERT classifier are compared under a loss-threshold MIA, with utility measured by development accuracy and macro F1. DistilBERT reached 0.9466 accuracy and 0.9460 macro F1 against 0.8756 and 0.8727 for Logistic Regression, yet both models leaked membership signal (Attack AUC 0.5615 and 0.5800, respectively). Two mitigations were tested. Stronger regularization reduced leakage for Logistic Regression at a visible utility cost, whereas fine-tuning DistilBERT for 2 epochs instead of 3 reduced leakage with negligible accuracy loss. Lightweight training adjustments can improve the privacy-utility trade-off without complex defenses.

cs.CR↗

arXiv2026

Empirical Evaluation of Data Poisoning Attacks in Supervised Learning

Data poisoning corrupts training data to degrade a model or to plant attacker-controlled behavior. This study evaluates two representative training-time attacks, label flipping and backdoor poisoning, on MNIST and Fashion-MNIST with three baseline classifiers: Logistic Regression, Linear SVM, and Random Forest. Clean training is compared with poisoning rates of 5%, 10%, and 20% using clean-test accuracy, macro-precision, macro-recall, macro-F1, and, for backdoors, attack success rate. Label flipping caused clear degradation, largest for Logistic Regression and Linear SVM, while Random Forest stayed comparatively stable. Backdoor poisoning reached attack success rates from 0.9667 to 1.0000 on both datasets and all three models while often keeping clean-test performance near baseline. The results separate indiscriminate poisoning, which shows up in standard metrics, from targeted backdoor poisoning, which stays comparatively stealthy while embedding highly effective malicious behavior, and they support security-oriented evaluation beyond conventional clean-test metrics.

cs.CR↗

arXiv2026

DeFiFusion: Combining Transaction Events with Smart Contracts to Detect Price Manipulation Attacks

Decentralized Finance (DeFi) has emerged as a rapidly growing blockchain-based financial service, where market transaction dynamics and underlying smart contract logic are intricately intertwined. This autonomous interplay, while eliminating centralized intermediaries, significantly expands the vulnerability surface of DeFi protocols to Price Manipulation Attacks (PMAs), which have already inflicted catastrophic financial losses. Despite their gravity, existing detection paradigms suffer from fundamental limitations. Transaction-centric methods lack awareness of contract execution semantics, making them prone to false positives under legitimate market volatility, while static contract analyses ignore real transaction behaviors and frequently report vulnerabilities that are infeasible to exploit in practice. We present DeFiFusion, a dual-modal PMA detection framework that closes this gap by jointly modeling transaction events and smart contract semantics within a unified pipeline. Our core insight is that PMA maliciousness emerges only from the interaction between transaction behaviors and the contract logic they exploit; neither signal suffices in isolation. Accordingly, we derive price-manipulation-aware event encoding for extracting fine-grained temporal and economic features tailored to manipulation patterns. We further introduce LLM-based contract semantic extraction to supply the execution-logic context that prior behavioral methods lack. To fuse these modalities, we propose a Dual-Modal Projection-Fusion Transformer with T5-style relative positional encoding, capturing the cyclic multi-stage execution structures that distinguish PMAs from benign market activity. Extensive experiments demonstrate that DeFiFusion consistently achieves state-of-the-art detection performance, effectively recalling 222 of the 225 PMA cases while maintaining a precision of 96.10%.

cs.CR↗