Buzz to Boom: Detecting Message Progression Vulnerabilities in Electron Applications via Segmented Directed Fuzzing
- URL: http://arxiv.org/abs/2607.20698v1
- Date: Wed, 22 Jul 2026 20:11:33 GMT
- Title: Buzz to Boom: Detecting Message Progression Vulnerabilities in Electron Applications via Segmented Directed Fuzzing
- Abstract summary: Electron is a popular framework for building cross-platform desktop applications.<n>Message propagation behavior is characterized as Message Vulnerabilities (MPVs)<n>We present Proton, a framework for detecting cross-process exploits via message passing.
- Score: 26.33861052551835
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Electron is a popular framework for building cross-platform desktop applications using web technologies. Such applications consist of multiple processes with different privilege levels that communicate via message passing. When inter-process messages carry attacker-controlled inputs, they can propagate across processes and reach privileged APIs, e.g., command execution. Such a message propagation behavior is characterized as Message Progression Vulnerabilities (MPVs). The exploitation of MPVs is challenging because it often requires multiple steps, e.g., first arbitrary code execution in one process via message passing, and then command injection in another process using another message crafted in the first process. To our knowledge, existing works on Electron security only study unsafe configurations and malicious Document Object Model (DOM) content, i.e., they cannot detect or exploit these vulnerabilities that need to be triggered by complex cross-process exploits via message passing. We present Proton, a segmented directed fuzzing framework for detecting MPVs. Our key insight is to decompose end-to-end fuzzing into per-process segments along message-passing boundaries, where the goals of fuzzing each segment are either: (i) reaching a sink in the current process or (ii) propagating the payload to the next process, to enable the exploration of another process. In the second case, the messages seed the corpus of the next segment. Finally, Proton synthesizes crash inputs from each process to validate end-to-end exploits. We evaluate Proton against 589 real-world Electron applications, resulting in 23 zero-day MPVs. Among them, 22 lead to OS command execution, including projects with over 50k GitHub stars. We responsibly disclosed all findings. To date, we have received 13 acknowledgments, 11 fixes, and 11 CVEs, including a bug bounty from Vercel.
Related papers
- LMSM: LLM Security Framework Inspired by Linux Security Modules [56.93644694627783]
Large language models (LLMs) are increasingly deployed with layered defenses, yet malicious prompts can still bypass them.<n>We present Language Model Security Modules (LMSM), a security framework that adapts the separation behind Linux Security Modules (LSM) to LLM serving.
arXiv Detail & Related papers (2026-08-26T12:13:05Z) - Hardware Keystores for AI Agent Signing Workflows: A Zero-Trust MCP Enforcement Architecture [0.0]
Hardware-confined keys are accessible through a vendor-neutral PKCS interface.<n> Hardware confinement is the primary contribution.<n>We evaluate against 12 injection scenarios derived from AgentDojo's ImportantAttack template.
arXiv Detail & Related papers (2026-08-06T15:04:26Z) - Send and Pretend: Exploiting Transcript Consistency Issues in End-to-End Encrypted Group Chats [1.8831329126144525]
Threema, WhatsApp, Signal and iMessage have recently introduced polls to aid agreement processes in groups.<n>This implicitly sets the expectation that all participants see the same outcome and thus have the same view of the conversation.<n>In this work, we demonstrate that today's major E2EE messengers do not guarantee any form of transcript consistency for group chats.
arXiv Detail & Related papers (2026-07-29T22:54:21Z) - PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution [49.776611937968]
We present PhyAgentOS, a foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services.<n>Its Session-Centered treats a session, not an action, as the minimum unit of scheduling, compatibility, supervised execution, evidence collection, and acceptance.<n>SessionVerifier distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.<n> Benchmarking reuses the deployment session and verification path, so results trace to real execution.
arXiv Detail & Related papers (2026-07-18T04:46:53Z) - Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens [65.53494487819053]
Claw-like AI agents (e.g., OpenClaw) are always-on processes with persistent access to credentials, files, tools, and external services.<n>We adopt a computer-system analogy: treating a Claw-like agent as an agentic computer system whose gateway runtime plays an OS-like mediation role.<n>We develop SafeClawArena, a benchmark of 406 adversarial tasks across four attack surfaces.
arXiv Detail & Related papers (2026-06-29T18:00:45Z) - RoboProcessBench: Benchmarking Process-Aware Understanding in Vision-Language Robotic Manipulation [59.10505906182782]
Vision-language models (VLMs) are increasingly explored as visual critics, reward generators, and failure detectors in robotic manipulation.<n>Existing evaluations fail to test whether VLMs possess fine-grained process understanding.<n>We present RoboProcessBench, a benchmark for process-aware understanding in vision-language robotic manipulation.
arXiv Detail & Related papers (2026-06-11T08:20:42Z) - PROJECTMEM: A Local-First, Event-Sourced Memory and Judgment Layer for AI Coding Agents [0.4779196219827507]
We present projectmem, an open-source, local-first memory and judgment layer for AI coding agents.<n> projectmem records development as an append-only, plain-text event log of typed events.<n>We frame this as Memory-as-Governance: memory that does not merely answer the agent but acts on its next action.
arXiv Detail & Related papers (2026-06-10T17:02:56Z) - AEGIS: No Tool Call Left Unchecked -- A Pre-Execution Firewall and Audit Layer for AI Agents [4.963079926145645]
AEGIS is a pre-execution firewall and audit layer for AI agents.<n>It interposes on the tool-execution path and applies a three-stage pipeline.<n>High-risk calls can be held for human approval, and all decisions are recorded in a tamper-evident audit trail.
arXiv Detail & Related papers (2026-03-13T03:49:12Z) - Zero-Permission Manipulation: Can We Trust Large Multimodal Model Powered GUI Agents? [6.9619059967556725]
Action Rebinding is a novel attack that allows a seemingly-benign app with zero dangerous permissions to rebind an agent's execution.<n>We weaponize the agent's task-recovery logic and Android's UI state preservation to orchestrate programmable, multi-step attack chains.<n>Our results demonstrate a 100% success rate for atomic action rebinding and the ability to reliably orchestrate multi-step attack chains.
arXiv Detail & Related papers (2026-01-18T10:54:54Z) - VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents [74.6761188527948]
Computer-Use Agents (CUAs) with full system access pose significant security and privacy risks.<n>We investigate Visual Prompt Injection (VPI) attacks, where malicious instructions are visually embedded within rendered user interfaces.<n>Our empirical study shows that current CUAs and BUAs can be deceived at rates of up to 51% and 100%, respectively, on certain platforms.
arXiv Detail & Related papers (2025-06-03T05:21:50Z) - Automated detection of atomicity violations in large-scale systems [8.31445151968485]
Atomicity violations in interrupt-driven programs pose a significant threat to software reliability in safety-critical systems.<n>We propose CLOVER, a multi-agent framework for detecting atomicity violations in real-world interrupt-driven programs.
arXiv Detail & Related papers (2025-04-01T08:13:29Z) - Robust and Verifiable Information Embedding Attacks to Deep Neural
Networks via Error-Correcting Codes [81.85509264573948]
In the era of deep learning, a user often leverages a third-party machine learning tool to train a deep neural network (DNN) classifier.
In an information embedding attack, an attacker is the provider of a malicious third-party machine learning tool.
In this work, we aim to design information embedding attacks that are verifiable and robust against popular post-processing methods.
arXiv Detail & Related papers (2020-10-26T17:42:42Z) - FCOS: A simple and strong anchor-free object detector [111.87691210818194]
We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion.
Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes.
In contrast, our proposed detector FCOS is anchor box free, as well as proposal free.
arXiv Detail & Related papers (2020-06-14T01:03:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.