arXiv subjects

Lukasz Olejnik

Publications and source records attributed to Lukasz Olejnik.

5 recordsLinked to original sources

arXiv2026

AI Grinding for Fun and Cryptanalysis

We present an autonomous cryptanalysis workflow in which agents generate, test, and refine hypotheses before human review. The autonomous stage returns reproducible candidates with exact witnesses, controls, code, and run records. A researcher then decides whether the evidence establishes a break, defect, or coverage gap. Two failure modes recur. First, a public algebraic map or input representation erases or exposes a relation that a construction must hide. Examples include multiplication by zero, boundary coefficients of a polynomial product, quotients, characters, Schur squares, and variable-length byte encodings without boundaries. Second, a simulator, error law, or parameter certification uses a distribution different from the one claimed. Several targets fail in both ways. Every result has an exact witness and a discriminating control; every stated boundary has a proof. Three further targets yielded no attack but support narrower guarantees than a generic reading suggests. Eight published constructions fail at stated parameters or claims. A Ring-LWR commitment opens to every message with probability one. One ciphertext reveals two middle-product encryption rows. A lattice e-voting protocol loses receipt-freeness. A permutation-recovery attack against updatable encryption extends by linear algebra to the old decryption key. An explicit normal basis splits a degree-63 instance into seven degree-nine instances. A signature hash outside the lattice setting maps two printable equal-length messages to the same digest. A rerandomisable scheme's accept bit is a threshold oracle on its decryption noise. Separately, a group-ring decision claim and a multivariate MinRank hardening fail at the assumption or accounting level rather than as complete construction breaks. Each failure occurs one level above its supporting assumption.

cs.CR↗

arXiv2026

Key Recovery from Residue-Confined Errors in Pradhan CRT-RLWE

We show that the CRT-FHE scheme of Pradhan et al.\ is insecure for laws within its assumed error distribution range. The secret key follows from the public key by a single ring inversion whenever the public multiplier is a unit. The plaintext is recovered from any ciphertext under such a law without the secret key, for every multiplier, giving chosen-plaintext advantage $1/2$. We further show that the transformation from ordinary Ring-LWE to CRT-RLWE does not preserve the error distribution, so it does not establish that CRT-RLWE is at least as hard as Ring-LWE. One mechanism underlies both. The Chinese remainder theorem (CRT) function is reduced modulo $p_1p_2$ while its output is used modulo a coprime modulus $q$, so under every zero-preserving section an error in $p_2\R$ encodes to zero. The law $p_2B_1$ is so confined, meets the stated conditions, and decrypts correctly. Confinement is not a weakness of scale: scaling any baseline law by $p_2$ leaves its ordinary Ring-LWE problem exactly equivalent, while the reduced encoder destroys every error it produces. The reduction discrepancy is a multiple of $p_1p_2$ and not of $q$, so the small-error premise of the proof cannot remove it, and at the reported parameters a single error coefficient refutes the identity while satisfying that premise. The centered binomial $B_2$ separates the coefficient laws at total variation distance $3/8$, and at the reported dimension that distance between the induced polynomial laws is exponentially close to one.

cs.CR↗

arXiv2026

IO Factory: Simulating AI-Enabled Influence Campaigns at Scale

We introduce IO Factory, an AI-driven framework for simulating information and influence campaigns as fully integrated, traceable processes. The threat of digital manipulation now extends beyond persuasive text from individual language models to AI swarms, i.e., persistent groups of coordinated agents that adapt to platform feedback and disguise organized campaigns as ordinary social interaction. Because such campaigns cannot be identified from isolated messages alone, they must be analyzed across a continuous spectrum of planning, platform action, exposure, interpretation, measurement, and adaptation. IO Factory represents this process inside a controlled simulated platform, linking actor roles, platform actions, exposure records, structured model-based evaluations, and configured changes in the simulated population. We implement the architecture and evaluate it across configurations of up to 100,000 agents. The results show that IO Factory executes campaign timelines at scale and produces inspectable evidence of exposure and measured movement in configured belief variables. By recording the actors, objectives, action constraints, exposure paths, and measurement rules used in each run, IO Factory supports reproducible research and red-team analysis of coordinated influence.

cs.AI↗

arXiv2025

AI Propaganda factories with language models

AI-powered influence operations can now be executed end-to-end on commodity hardware. We show that small language models produce coherent, persona-driven political messaging and can be evaluated automatically without human raters. Two behavioural findings emerge. First, persona-over-model: persona design explains behaviour more than model identity. Second, engagement as a stressor: when replies must counter-arguments, ideological adherence strengthens and the prevalence of extreme content increases. We demonstrate that fully automated influence-content production is within reach of both large and small actors. Consequently, defence should shift from restricting model access towards conversation-centric detection and disruption of campaigns and coordination infrastructure. Paradoxically, the very consistency that enables these operations also provides a detection signature.

cs.CR↗

arXiv2021

The CNAME of the Game: Large-scale Analysis of DNS-based Tracking Evasion

Online tracking is a whack-a-mole game between trackers who build and monetize behavioral user profiles through intrusive data collection, and anti-tracking mechanisms, deployed as a browser extension, built-in to the browser, or as a DNS resolver. As a response to pervasive and opaque online tracking, more and more users adopt anti-tracking tools to preserve their privacy. Consequently, as the information that trackers can gather on users is being curbed, some trackers are looking for ways to evade these tracking countermeasures. In this paper we report on a large-scale longitudinal evaluation of an anti-tracking evasion scheme that leverages CNAME records to include tracker resources in a same-site context, effectively bypassing anti-tracking measures that use fixed hostname-based block lists. Using historical HTTP Archive data we find that this tracking scheme is rapidly gaining traction, especially among high-traffic websites. Furthermore, we report on several privacy and security issues inherent to the technical setup of CNAME-based tracking that we detected through a combination of automated and manual analyses. We find that some trackers are using the technique against the Safari browser, which is known to include strict anti-tracking configurations. Our findings show that websites using CNAME trackers must take extra precautions to avoid leaking sensitive information to third parties.

cs.CR↗