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Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model An AI Agent Execution Environment to Safeguard User Data TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
Manifest V3 Unveiled: Navigating the New Era of Browser E...
Nikolaos Pantelaios, Alexandros Kapravelos · 2024-04-12 · via cs.CR updates on arXiv.org

Introduced over a decade ago, Chrome extensions now exceed 200,000 in number. In 2020, Google announced a shift in extension development with Manifest Version 3 (V3), aiming to replace the previous Version 2 (V2) by January 2023. This deadline was later extended to January 2025. The company's decision is grounded in enhancing three main pillars: privacy, security, and performance. This paper presents a comprehensive analysis of the Manifest V3 ecosystem. We start by investigating the adoption rate of V3, detailing the percentage of adoption from its announcement up until 2024. Our findings indicate, prior to the 2023 pause, less than 5% of all extensions had transitioned to V3, despite the looming deadline for the complete removal of V2, while currently nine out of ten new extensions are being uploaded in Manifest V3. Furthermore, we compare the security and privacy enhancements between V2 and V3 and we evaluate the improved security attributable to V3's safer APIs, examining how certain APIs, which were vulnerable or facilitated malicious behavior, have been deprecated or removed in V3. We dynamically execute 517 confirmed malicious extensions and we see a 87.8% removal of APIs related to malicious behavior due to the improvements of V3. We discover that only 154 (29.8%) of these extensions remain functional post-conversion. This analysis leads to the conclusion that V3 reduces the avenues for abuse of such APIs. However, despite the reduction in APIs associated with malicious activities, the new Manifest V3 protocol is not immune to such behavior. Our research demonstrates, through a proof of concept, the adaptability of malicious activities to V3. After the proof of concept changes are applied, we showcase 290 (56%) of the examined malicious extensions retain their capability to conduct harmful activities within the V3 framework.