Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.39093

Do Open-Source Video Ad Clients Follow VAST 4.3? An Empirical Study of Player Conformance

Prashant Chaudhary
View original source ↗Read source document ↗Export metadata ↓

Abstract

Context. Video ads reach streaming and connected-TV apps through VAST documents, and companies count ads from the HTTP requests the player's client library sends. A tracking error can leave playback intact and surface later as disagreeing counts. Objective. To measure how far open-source VAST clients meet the player duties of VAST 4.3, whether verdicts depend on the runtime, what generated inputs add, and when deviations were introduced. Method. A catalog of 108 requirements was extracted from the specification. Six open-source clients in eight configurations ran against 69 crafted test vectors and the IAB's sample tags, under Node.js and in five browsers on three engines, with every request recorded. Grids, fuzzing and a sweep over 36 releases extend the catalog; Google's closed IMA SDK is a comparison. Results. The open-source clients have 78 findings and 3 to 21 must-level deviations per configuration. None of the tested open-source clients that follow redirect (Wrapper) chains fills the required macros in a Wrapper's request; the archived IMA 3.794.0 configuration tested does. One library's timeout throws an exception no caller can catch, ending a Node.js process. In a pod (ads played back to back), one player credits a finishing ad's completion to the next ad, fixed in release 5.3.0. A library can conform under jsdom and deviate in every tested Chrome- and Safari-family browser. Conclusions. The tested clients diverge on the duties other companies use to count and diagnose ads. A shared conformance suite run in real browsers could expose most graded deviations; the harness and vectors are released.

Explore related subjects

cs.SEcs.MMcs.NI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Prashant Chaudhary. 2026-10-29. Do Open-Source Video Ad Clients Follow VAST 4.3? An Empirical Study of Player Conformance. https://arxiv.org/abs/2610.39093

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

KEEP EXPLORING

Related papers

arXiv2026

CodeArena: A Collective Evaluation Platform for LLM Code Generation

Large Language Models (LLMs) have reshaped code generation by synergizing their exceptional comprehension of natural language and programming syntax, thereby substantially boosting developer productivity. These advancements have prompted numerous efforts to quantitatively evaluate their coding capabilities. However, persistent challenges, such as benchmark leakage, data dissipation, and limited system accessibility, continue to impede a timely and accurate assessment. To address these limitations, we introduce CodeArena, an online evaluation framework tailored for LLM code generation. The key innovation is a collective evaluation mechanism, which dynamically recalibrates individual model scores based on the holistic performance of all participating models, mitigating score biases caused by widespread benchmark leakage. In addition, CodeArena ensures open access to all submitted solutions and test cases and provides automation-friendly APIs to streamline the code evaluation workflow. Our main contributions are: (1) a collective evaluation system for unbiased assessment, (2) a public repository of solutions and test cases, and (3) automation-ready APIs for seamless integration.

cs.SE↗
arXiv2026

Towards Functional Correctness of Code Models with Selective Generation

The hallucination of code generation models hinders their applicability to systems requiring higher safety standards. One critical bottleneck in addressing code hallucination is the difficulty of identifying the functional correctness of generated code, due to its unnatural form. We address this core bottleneck by automatically generating unit tests using dynamic code analysis tools, leveraging the \emph{executable nature} of code. Accordingly, we propose a \emph{selective code generator} that abstains from uncertain generations -- based on the functional correctness evaluated by generated unit tests -- to theoretically control the correctness among non-abstained answers, \ie the false discovery rate. Finally, we propose to use generated unit tests in evaluation as well as in learning for precise code evaluation, calling this paradigm \emph{FuzzEval}. We demonstrate the efficacy of our method along with the controllability of code hallucination and reasonable selection efficiency.

cs.SE↗
arXiv2026

Red Teaming Program Repair Agents: When Correct Patches can Hide Vulnerabilities

LLM-based agents are increasingly deployed for software maintenance tasks such as automated program repair (APR). APR agents automatically fetch GitHub issues and use backend LLMs to generate patches that fix the reported bugs. However, existing work primarily focuses on the functional correctness of APR-generated patches, whether they pass hidden or regression tests, while largely ignoring potential security risks. Given the openness of platforms like GitHub, where any user can raise issues and participate in discussions, an important question arises: Can an adversarial user submit a valid issue on GitHub that misleads an LLM-based agent into generating a functionally correct but vulnerable patch? To answer this question, we propose SWExploit, which generates adversarial issue statements designed to make APR agents produce patches that are functionally correct yet vulnerable. SWExploit operates in three main steps: (1) program analysis to identify potential injection points for vulnerable payloads; (2) adversarial issue generation to provide misleading reproduction and error information while preserving the original issue semantics; and (3) iterative refinement of the adversarial issue statements based on the outputs of the APR agents. Empirical evaluation on three agent pipelines and five backend LLMs shows that SWExploit can produce patches that are both functionally correct and vulnerable (the attack success rate on the correct patch could reach 0.91, whereas the baseline ASRs are all below 0.20). Based on our evaluation, we are the first to challenge the traditional assumption that a patch passing all tests is inherently reliable and secure, highlighting critical limitations in the current evaluation paradigm for APR agents.

cs.SE↗