Searcharxiv⌕ Search

arXiv · 2609.33099

Never Emitted: Reporter Attribution in GitHub's Machine-Readable Vulnerability Records

Abstract

The CVE record format defines a credits container that names who found or reported a vulnerability, with a typed role per entry. The OSV schema defines an equivalent field. GitHub, which assigns CVE identifiers for advisories in its ecosystems, collects this information from reporters, requires them to accept it, displays it on the advisory page, and serves it through its own REST API. It emits it into neither standardized format. Across 238 GitHub-assigned CVE records whose linked advisory publicly credits at least one party, zero carry a credits container, while all 238 carry metrics and problemTypes, two fields the CVE schema leaves optional exactly as it leaves credits. Across 302 advisories in the same pool we retrieved GitHub's own OSV export file, and zero carry a credits field. The omission is not a property of either format: the Erlang Ecosystem Foundation populates the CVE field on 18 of 18 records in the same pool using a freely available client for CVE Services. In a census of all 4,889 published CVE records in a two-week window, 46.9% carry credits, GitHub's rate is 0 of 570 without any advisory filter, and assigner behaviour is concentrated at the extremes without being exhausted by them: 16 assigners emit the field on no record and 13 on essentially every record, while 8 assigners covering 23% of the records sit in between. A request to close the gap has been open since January 2023; GitHub's stated reason for deferring it is quoted verbatim. We further show that the NVD API schema defines no credits field, so attribution that CNAs do emit does not reach the database most tooling consumes: of 43 credit-bearing records traced from advisory to CVE record to NVD, none retained it. We release the collection scripts and a frozen snapshot of every API response.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Anas Mohiuddin Syed. 2026-09-27. Never Emitted: Reporter Attribution in GitHub's Machine-Readable Vulnerability Records. https://arxiv.org/abs/2609.33099

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

KEEP EXPLORING

Related papers

SoK: Cryptocurrency Mixing and Anonymity - Architectures, Threat Models, Operational Aspects and Security

Public blockchains record transaction histories that enable address clustering, taint analysis, and cross-service attribution, thereby motivating the development of mixers and privacy layers. Our work presents a structured scoping review of 22 representative systems, defining a common unlinkability objective and five adversary archetypes. We evaluate these systems against a taxonomy of attack surfaces, including chain analysis, timing inference, custodial compromise, coordination abuse, network metadata, and trusted execution compromise. While nominal anonymity-set size and cryptographic strength characterize privacy in theory, effective anonymity in practice depends on transaction denominations, cover traffic, relayer behavior, and compliance-interface design. Distinguishing nominal from effective anonymity, we derive four core lessons: (1) Privacy is strongest when integrated into everyday transactions, since standalone mixing creates an easily profiled user subset; (2) Trust points, including operators, peer quorums, and hardware enclaves, must be explicit so users know who can break privacy; (3) Network metadata, including gas funding and timing, must be treated formally as protocol data in privacy evaluations; and (4) Compliance should use auditable cryptographic predicates for selective disclosure rather than broad operator discretion. Ultimately, our systematization clarifies the strengths, failures, and future requirements of blockchain privacy architectures.

cs.CR↗

Decoding One Safety Trigger Token for Balancing Safety and Usability in Large Language Models

Large Language Models (LLMs) have been extensively used across diverse domains, including virtual assistants, automated code generation, and scientific research. However, they remain vulnerable to jailbreak attacks, which manipulate the models into generating harmful responses despite safety alignment. Recent studies have shown that current safety-aligned LLMs undergo shallow safety alignment. In this work, we conduct an in-depth investigation into the underlying mechanism of this phenomenon and reveal that it manifests through learned ''safety trigger tokens'' that activate the model's safety patterns when paired with the specific input. Through both analysis and empirical verification, we further demonstrate the high similarity of the safety trigger tokens across different harmful inputs. Accordingly, we propose D-STT, a simple yet effective defense algorithm that identifies and explicitly decodes safety trigger tokens of the given safety-aligned LLM to activate the model's learned safety patterns. In this process, the safety trigger is constrained to a single token, which effectively preserves model usability by introducing minimum intervention in the decoding process. Extensive experiments across diverse jailbreak attacks and benign prompts demonstrate that D-STT significantly reduces output harmfulness while preserving model usability and incurring negligible response time overhead, outperforming ten baseline methods.

cs.CR↗

A traffic analysis attack against Introduction Protocol and Onion Services

Tor onion services rely on long-lived introduction circuits to support anonymous rendezvous between clients and services. Although Tor incorporates defenses against traffic analysis, the introduction protocol retains deterministic routing structure that can be exploited by an adversary. We present a practical intersection attack against Tor introduction circuits that over repeated interactions can identify each hop from the introduction point toward the onion service while requiring observation at only one relay per stage. The attack repeatedly probes the target service and intersects sets of destination IP addresses observed within narrowly bounded INTRODUCE1-RENDEZVOUS2 intervals, without assuming global visibility or access to packet payloads. Our traffic-analysis technique identifies with certainty the next relay in the path to target at each stage, thereby revealing a gap in Tor's privacy model, which is intended to resist traffic-analysis attacks in which an adversary uses traffic patterns to determine which points in the network to observe or attack. We evaluate the attack's feasibility through live-network experiments using a self-operated onion service and relays. To support data minimization, we implement a Tor-compatible plugin that computes intersections online over pseudonymized data retained only in volatile memory. Our experiments show reliable convergence in practice, with convergence rate influenced by relay consensus weight and time-varying background traffic. We further assess practicality under a partial-global adversary model and discuss the implications of geographic concentration in Tor relay selection weight across cooperating jurisdictions.

cs.CR↗