arXiv subjects

Orr Dunkelman

Publications and source records attributed to Orr Dunkelman.

10 recordsLinked to original sources

arXiv2026

CryptanalysisBench: Can LLMs do Cryptanalysis?

Cryptanalysis - the task of finding attacks against cryptographic schemes - sits at the intersection of mathematical reasoning and cybersecurity, two areas where LLMs have advanced fastest. Cryptanalysis represents both a clean testbed for frontier reasoning (as practical attacks can be automatically verified) and a domain with unusually high stakes, since the primitives under study underpin our digital security. In this paper we ask whether LLMs can do cryptanalysis, and find that the answer is increasingly yes. We introduce CryptanalysisBench, 191 tasks across six families of cryptographic primitives (block ciphers, hash functions, etc.) drawn primarily from four NIST standardization competitions. Our benchmark consists of three tiers: (i) primitives with known practical breaks; (ii) primitives with no known practical break, evaluated both at full strength and as scaled-down variants; and (iii) a challenge set of production primitives at the frontier of cryptanalysis. Five frontier models (Claude Opus 4.8, Sonnet 5, Mythos 5, GPT 5.5, and the open-weights GLM 5.2) break 65%-86% of Tier 1 schemes, 6-12 Tier-2 schemes at full strength, and 24-61 across all scaled-down variants. Beyond deriving known results, models produce novel cryptanalysis, such as a key-recovery attack that exploits a design flaw in the SpoC AEAD and an error in KINDI's published CCA-security proof, both to the best of our knowledge not previously known. We release CryptanalysisBench as a tool to help track if (or when) AI cryptanalysis becomes a serious factor and as a scaffold for stress-testing candidate schemes before deployment. The attacks that the benchmark already surfaces are an early snapshot of a fast-moving frontier that may soon match, and in places exceed, the published state of the art.

cs.CR↗

arXiv2026

Reconstructing Protected Biometric Templates from Binary Authentication Results

Biometric data is considered to be very private and highly sensitive. As such, many methods for biometric template protection were considered over the years -- from biohashing and specially crafted feature extraction procedures, to the use of cryptographic solutions such as Fuzzy Commitments or the use of Fully Homomorphic Encryption (FHE). A key question that arises is how much protection these solutions can offer when the adversary can inject samples, and observe the outputs of the system. While for systems that return the similarity score, one can use attacks such as hill-climbing, for systems where the adversary can only learn whether the authentication attempt was successful, this question remained open. In this paper, we show that it is indeed possible to reconstruct the biometric template by just observing the success/failure of the authentication attempt (given the ability to inject a sufficient amount of templates). Our attack achieves negligible template reconstruction loss and enables full recovery of facial images through a generative inversion method, forming a pipeline from binary scores to high-resolution facial images that successfully pass the system more than 98\% of the time. Our results, of course, are applicable for any protection mechanism that maintains the accuracy of the recognition.

cs.CR↗

arXiv2025

Error Resilient Space Partitioning

A major research area in discrete geometry is to consider the best way to partition the $d$-dimensional Euclidean space $\mathbb{R}^d$ under various quality criteria. In this paper we introduce a new type of space partitioning that is motivated by the problem of rounding noisy measurements from the continuous space $\mathbb{R}^d$ to a discrete subset of representative values. Specifically, we study partitions of $\mathbb{R}^d$ into bounded-size tiles colored by one of $k$ colors, such that tiles of the same color have a distance of at least $t$ from each other. Such tilings allow for \emph{error-resilient} rounding, as two points of the same color and distance less than $t$ from each other are guaranteed to belong to the same tile, and thus, to be rounded to the same point. The main problem we study in this paper is characterizing the achievable tradeoffs between the number of colors $k$ and the distance $t$, for various dimensions $d$. On the qualitative side, we show that in $\mathbb{R}^d$, using $k=d+1$ colors is both sufficient and necessary to achieve $t>0$. On the quantitative side, we achieve numerous upper and lower bounds on $t$ as a function of $k$. In particular, for $d=3,4,8,24$, we obtain sharp asymptotic bounds on $t$, as $k \to \infty$. We obtain our results with a variety of techniques including isoperimetric inequalities, the Brunn-Minkowski theorem, sphere packing bounds, Bapat's connector-free lemma, and Čech cohomology.

cs.CG↗

arXiv2021

COVID-19 Datathon Based on Deidentified Governmental Data as an Approach for Solving Policy Challenges, Increasing Trust, and Building a Community: Case Study

Triggered by the COVID-19 crisis, Israel's Ministry of Health (MoH) held a virtual Datathon based on deidentified governmental data. Organized by a multidisciplinary committee, Israel's research community was invited to offer insights to COVID-19 policy challenges. The Datathon was designed to (1) develop operationalizable data-driven models to address COVID-19 health-policy challenges and (2) build a community of researchers from academia, industry, and government and rebuild their trust in the government. Three specific challenges were defined based on their relevance (significance, data availability, and potential to anonymize the data): immunization policies, special needs of the young population, and populations whose rate of compliance with COVID-19 testing is low. The MoH team extracted diverse, reliable, up-to-date, and deidentified governmental datasets for each challenge. Secure remote-access research environments with relevant data science tools were set on Amazon Web. The MoH screened the applicants and accepted around 80 participants, teaming them to balance areas of expertise as well as represent all sectors of the community. One week following the event, anonymous surveys for participants and mentors were distributed to assess overall usefulness and points for improvement. The 48-hour Datathon and pre-event sessions included 18 multidisciplinary teams, mentored by 20 data scientists, 6 epidemiologists, 5 presentation mentors, and 12 judges. The insights developed by the 3 winning teams are currently considered by the MoH as potential data science methods relevant for national policies. The most positive results were increased trust in the MoH and greater readiness to work with the government on these or future projects. Detailed feedback offered concrete lessons for improving the structure and organization of future government-led datathons.

cs.CY↗

arXiv2020

Fuzzy Commitments Offer Insufficient Protection to Biometric Templates Produced by Deep Learning

In this work, we study the protection that fuzzy commitments offer when they are applied to facial images, processed by the state of the art deep learning facial recognition systems. We show that while these systems are capable of producing great accuracy, they produce templates of too little entropy. As a result, we present a reconstruction attack that takes a protected template, and reconstructs a facial image. The reconstructed facial images greatly resemble the original ones. In the simplest attack scenario, more than 78% of these reconstructed templates succeed in unlocking an account (when the system is configured to 0.1% FAR). Even in the "hardest" settings (in which we take a reconstructed image from one system and use it in a different system, with different feature extraction process) the reconstructed image offers 50 to 120 times higher success rates than the system's FAR.

cs.CR↗

arXiv2019

Tight Bounds on Online Checkpointing Algorithms

The problem of online checkpointing is a classical problem with numerous applications which had been studied in various forms for almost 50 years. In the simplest version of this problem, a user has to maintain $k$ memorized checkpoints during a long computation, where the only allowed operation is to move one of the checkpoints from its old time to the current time, and his goal is to keep the checkpoints as evenly spread out as possible at all times. Bringmann et al. studied this problem as a special case of an online/offline optimization problem in which the deviation from uniformity is measured by the natural discrepancy metric of the worst case ratio between real and ideal segment lengths. They showed this discrepancy is smaller than $1.59-o(1)$ for all $k$, and smaller than $\ln4-o(1)\approx1.39$ for the sparse subset of $k$'s which are powers of 2. In addition, they obtained upper bounds on the achievable discrepancy for some small values of $k$. In this paper we solve the main problems left open in the above-mentioned paper by proving that $\ln4$ is a tight upper and lower bound on the asymptotic discrepancy for all large $k$, and by providing tight upper and lower bounds (in the form of provably optimal checkpointing algorithms, some of which are in fact better than those of Bringmann et al.) for all the small values of $k \leq 10$. In the last part of the paper we describe some new applications of this online checkpointing problem.

cs.CR↗

arXiv2019

DNS-Morph: UDP-Based Bootstrapping Protocol For Tor

Tor is one of the most popular systems for anonymous communication and censorship circumvention on the web, currently used by millions of users every day. This puts Tor as a target for attacks by organizations and governmental bodies whose goal is to hinder users' ability to connect to it. These attacks include deep packet inspection (DPI) to classify Tor traffic as well as legitimate Tor client impersonation (active probing) to expose Tor bridges. As a response to Tor-blocking attempts, the Tor community has developed Pluggable Transports (PTs), tools that transform the appearance of Tor's traffic flow. In this paper we introduce a new approach aiming to enhance the PT's resistance against active probing attacks, as well as white-listing censorship by partitioning the handshake of the PT from its encrypted communication. Thus, allowing mixing different PTs, e.g., ScrambleSuit for the handshake and FTE for the traffic itself. We claim that this separation reduces the possibility of marking Tor related communications. To illustrate our claim, we introduce DNS-Morph: a new method of transforming the handshake data of a PT by imitating a sequence of DNS queries and responses. Using DNS-Morph, the Tor client acts as a DNS client which sends DNS queries to the Tor bridge, and receives DNS responses from it. We implemented and successfully tested DNS-Morph using one of the PTs (ScrambleSuit), and verified its capabilities.

cs.CR↗

arXiv2019

A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance

The existence of adversarial examples in which an imperceptible change in the input can fool well trained neural networks was experimentally discovered by Szegedy et al in 2013, who called them "Intriguing properties of neural networks". Since then, this topic had become one of the hottest research areas within machine learning, but the ease with which we can switch between any two decisions in targeted attacks is still far from being understood, and in particular it is not clear which parameters determine the number of input coordinates we have to change in order to mislead the network. In this paper we develop a simple mathematical framework which enables us to think about this baffling phenomenon from a fresh perspective, turning it into a natural consequence of the geometry of $\mathbb{R}^n$ with the $L_0$ (Hamming) metric, which can be quantitatively analyzed. In particular, we explain why we should expect to find targeted adversarial examples with Hamming distance of roughly $m$ in arbitrarily deep neural networks which are designed to distinguish between $m$ input classes.

cs.LG↗

arXiv2016

HoneyFaces: Increasing the Security and Privacy of Authentication Using Synthetic Facial Images

One of the main challenges faced by Biometric-based authentication systems is the need to offer secure authentication while maintaining the privacy of the biometric data. Previous solutions, such as Secure Sketch and Fuzzy Extractors, rely on assumptions that cannot be guaranteed in practice, and often affect the authentication accuracy. In this paper, we introduce HoneyFaces: the concept of adding a large set of synthetic faces (indistinguishable from real) into the biometric "password file". This password inflation protects the privacy of users and increases the security of the system without affecting the accuracy of the authentication. In particular, privacy for the real users is provided by "hiding" them among a large number of fake users (as the distributions of synthetic and real faces are equal). In addition to maintaining the authentication accuracy, and thus not affecting the security of the authentication process, HoneyFaces offer several security improvements: increased exfiltration hardness, improved leakage detection, and the ability to use a Two-server setting like in HoneyWords. Finally, HoneyFaces can be combined with other security and privacy mechanisms for biometric data. We implemented the HoneyFaces system and tested it with a password file composed of 270 real users. The "password file" was then inflated to accommodate up to $2^{36.5}$ users (resulting in a 56.6 TB "password file"). At the same time, the inclusion of additional faces does not affect the true acceptance rate or false acceptance rate which were 93.33\% and 0.01\%, respectively.

cs.CR↗

arXiv2016

Breaching the Privacy of Israel's Paper Ballot Voting System

An election is a process through which citizens in liberal democracies select their governing bodies, usually through voting. For elections to be truly honest, people must be able to vote freely without being subject to coercion; that is why voting is usually done in a private manner. In this paper we analyze the security offered by a paper-ballot voting system that is used in Israel, as well as in several other countries around the world. we provide an algorithm which, based on publicly available information, breaks the privacy of the voters participating in such elections. Simulations based on real data collected in Israel show that our algorithm performs well, and can correctly recover the vote of up to 96% of the voters.

cs.CR↗