Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2607.06194

Claimed or Attested? A Commit-Signature Dataset and Identity Trust Tiers across the World of Code

Audris Mockus
View original source ↗Read source document ↗Export metadata ↓

Abstract

An author string in a git commit is free text the committer typed, so identity resolution over a global commit corpus rests on a claim that nothing in the commit verifies. A cryptographically signed commit is different: it binds the commit to a key the committer controls, and when that key ties back to a real-world identity the git identity becomes attested rather than merely claimed. We release the first commit-signature axis for the World of Code (WoC), extracted for the V2604 collection. The signature travels in the commit object's gpgsig header and is already carried, unparsed, in the commit-message field of the WoC commit tables, so the axis is a scan over existing tables rather than a re-read of the object database. Over the V2604 corpus of 5,866,595,698 commits, 17.59% carry a signature (PGP dominant at 98.96%, with a growing minority of SSH and X.509/sigstore signatures), or 1,031,721,316 signed commits. We release the per-commit signature map c2sigFull, a key-to-author graph gated so that shared organization and continuous-integration keys are separated from person keys, and A2trust, a per-identity attestation tier (unsigned, signed, real-world-bound, cross-corpus attested) that extends the published A2cls identity-class dataset. The signature axis is a precision anchor, not a coverage layer: signed commits skew toward recent and security-conscious developers, a population that overlaps the scholarly authors a bibliography join targets. We use the person keys to build a cryptographically grounded alias gold that calibrates the heuristic WoC alias map independently of hand-labeled pairs, and to attach an attestation provenance to science-to-software identity links. All artifacts are released as a self-contained, in dependently hosted replication package keyed to the WoC V2604 collection.

Explore related subjects

cs.CRcs.SE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Audris Mockus. 2026-07-07. Claimed or Attested? A Commit-Signature Dataset and Identity Trust Tiers across the World of Code. https://arxiv.org/abs/2607.06194

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

KEEP EXPLORING

Related papers

arXiv2026

UPPRESSO: Untraceable and Unlinkable Privacy-PREserving Single Sign-On Services

Single sign-on (SSO) allows a user to maintain only the credential for an identity provider (IdP) to log into multiple relying parties (RPs). However, SSO introduces privacy threats, as (a) a curious IdP could track a user's all visits to RPs, and (b) colluding RPs could learn a user's online profile by linking her identities across these RPs. This paper presents a privacypreserving SSO scheme, called UPPRESSO, to protect an honest user's online profile against (a) an honest-but-curious IdP and (b) malicious RPs colluding with other users. UPPRESSO proposes an identity-transformation approach to generate untraceable ephemeral pseudo-identities for an RP and a user from which the target RP derives a permanent account for the user, while the transformations also provide unlinkability. This approach protects the identities of the user and the target RPs in a login flow, while working compatibly with widely-deployed SSO protocols and providing services accessed from a commercial-off-the-shelf browser without plug-ins or extensions. We built a prototype of UPPRESSO on top of MITREid Connect, an open-source SSO system. The extensive evaluations show that it fulfills the security and privacy requirements of SSO with reasonable overheads.

cs.CR↗
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↗