USENIX Security2016Top-tier venue
Website-Targeted False Content Injection by Network Operators
Gabi Nakibly, Jaime Schcolnik, Yossi Rubin
Abstract
It is known that some network operators inject false content into users' network traffic. Yet all previous works that investigate this practice focus on edge ISPs (Internet Service Providers), namely, those that provide Internet access to end users. Edge ISPs that inject false content affect their customers only. However, in this work we show that not only edge ISPs may inject false content, but also core network operators. These operators can potentially alter the traffic of all Internet users who visit predetermined websites. We expose this practice by inspecting a large amount of traffic originating from several networks. Our study is based on the observation that the forged traffic is injected in an out-of-band manner: the network operators do not update the network packets in-path, but rather send the forged packets without dropping the legitimate ones. This creates a race between the forged and the legitimate packets as they arrive to the end user. This race can be identified and analyzed. Our analysis shows that the main purpose of content injection is to increase the network operators' revenue by inserting advertisements to websites. Nonetheless, surprisingly, we have also observed numerous cases of injected malicious content. We publish representative samples of the injections to facilitate continued analysis of this practice by the security community.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext dc26c81a-0873-4081-a517-217aaeae8679Cited by top-tier papers2
- Who Is Answering My Queries: Understanding and Characterizing Interception of the DNS Resolution PathBaojun Liu, Chaoyi Lu, Hai-Xin Duan, Ying Liu et al.USENIX Security 2018 · 67 citations
- Confundo: Learning to Generate Robust Poison for Practical RAG SystemsHaoyang Hu, Zhejun Jiang, Yueming Lyu, Junyuan Zhang et al.USENIX Security 2026 · 5 citations
Related papers
- Edge Caching as DifferentiationMuhammad Abdullah, Mughees Ur Rehman, Pavlos Nikolopoulos, Katerina J. ArgyrakiSIGCOMM 2025 · 1 citation
- Cached and Confused: Web Cache Deception in the WildSeyed Ali Mirheidari, Sajjad Arshad, Kaan Onarlioglu, Bruno Crispo et al.USENIX Security 2020
- A Large-scale Analysis of Content Modification by Open HTTP ProxiesGiorgos Tsirantonakis, Panagiotis Ilia, Sotiris Ioannidis, Elias Athanasopoulos et al.NDSS 2018 · 38 citations
- A Large-scale Measurement of In-Page Prompt Injections Against LLM Web AgentsSoheil Khodayari, Xuenan Zhang, Bhupendra Acharya, Giancarlo PellegrinoCCS 2026
- Bad Packets Come Back, Worse Ones Don'tPetros Gigis, Mark James Handley, Stefano VissicchioSIGCOMM 2024 · 2 citations
