Cross User/App Network Attacks - Hijacking TCP Connections and DNS Cache Poisoning via a Malicious User/App (Extended Version)
- URL: http://arxiv.org/abs/2609.09345v2
- Date: Thu, 10 Sep 2026 06:29:54 GMT
- Title: Cross User/App Network Attacks - Hijacking TCP Connections and DNS Cache Poisoning via a Malicious User/App (Extended Version)
- Abstract summary: Off-path attacks against TCP and DNS (over UDP) client-server connections are generally considered impractical nowadays.<n>We demonstrate that an unprivileged malicious application running on the client can enable powerful network attacks against such connections.<n>We show how such a local--remote collaboration between the malicious application and a remote adversary allows inference of sensitive connection state.
- Score: 4.325717217536015
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Off-path network attacks against TCP and DNS (over UDP) client-server connections are generally considered impractical nowadays, due to built-in security features in these protocols, e.g. randomized TCP (initial) sequence numbers and randomized UDP source ports, respectively. In this work, we refute this presumption by demonstrating that an unprivileged malicious application running on the client (but practically off-path), when combined with a remote off-path adversary, can enable powerful network attacks against such connections. We show how such a local--remote collaboration between the malicious application and a remote adversary allows inference of sensitive connection state, including TCP sequence numbers and DNS stub-resolver UDP source ports. Our attacks exploit standard socket API calls such as bind(), protocol mechanisms such as IP options, and operating system features such as cBPF and procfs to infer the TCP initial sequence number (ISN) and the UDP source port in use by the connection of interest. Specifically, we take advantage of certain properties of the ISN generation algorithm as implemented in major operating systems. We demonstrate TCP connection hijacking in Linux, Android, Windows, macOS and iOS, and DNS cache poisoning against Windows, Android and the popular systemd-resolved DNS stub resolver in Linux. We evaluate our techniques across multiple operating systems and realistic deployment settings, including environments behind port-preserving NAT-integrated routers. We disclosed our techniques to Microsoft, Apple, Linux and Google, which led to the release of several patches.
Related papers
- 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) - Secrets Best Not Shared: DNS Privacy Enhancements for the Constrained IoT [2.3195032320229503]
Attackers often identify DNS traffic to disrupt or compromise Internet services.<n>We consider IETF protocols designed for resource-constrained IoT devices.<n>We show that DNS over CoAP with equalized packet lengths, block-wise transfer, and header compression reduces the accuracy of identifying DNS frames to 86%.
arXiv Detail & Related papers (2026-06-08T19:22:30Z) - DIST-FL: Enhancing Security for TEE-based Aggregation in Federated Learning [37.95865837498057]
Server-side adversaries can still manipulate client selection and replay aggregation to compromise system robustness and privacy.<n>We present DIST-FL, a distributed system of servers guarded by multiple TEEs forming an append-only ledger for privacy-preserved, robust FL aggregation.
arXiv Detail & Related papers (2026-06-03T14:01:10Z) - Invisible Adversaries: A Systematic Study of Session Manipulation Attacks on VPNs [29.461096043276104]
We present three session manipulation attacks targeting TCP and UDP traffic tunneled through VPNs.<n>The attacker who only connects to the same VPN server can launch denial-of-service attacks, hijack TCP connections of other clients, or inject forged DNS responses into their queries.<n>We evaluate these attacks against five popular connection tracking frameworks across different OSes and nine major commercial VPN providers.
arXiv Detail & Related papers (2026-04-05T12:43:08Z) - Off-Path TCP Exploits: PMTUD Breaks TCP Connection Isolation in IP Address Sharing Scenarios [21.746759860459136]
Path MTU Discovery (PMTUD) and IP address sharing are integral aspects of modern Internet infrastructure.<n>We reveal that PMTUD is inadequately designed to handle IP address sharing, creating vulnerabilities that attackers can exploit to perform off-path TCP hijacking attacks.
arXiv Detail & Related papers (2025-09-15T12:19:02Z) - Collusion Resistant DNS With Private Information Retrieval [42.34183823376613]
We propose PDNS, a DNS extension leveraging single-server Private Information Retrieval to strengthen privacy guarantees.<n>PDNS achieves acceptable performance (2x faster than DoH over Tor with similar privacy guarantees) and strong privacy guarantees today.
arXiv Detail & Related papers (2025-07-28T13:17:25Z) - MIP against Agent: Malicious Image Patches Hijacking Multimodal OS Agents [60.92962583528122]
Recent advances in operating system (OS) agents have enabled vision-language models (VLMs) to directly control a user's computer.<n>We uncover a novel attack vector against these OS agents: Malicious Image Patches (MIPs)<n>MIPs adversarially perturbed screen regions that, when captured by an OS agent, induce it to perform harmful actions by exploiting specific APIs.
arXiv Detail & Related papers (2025-03-13T18:59:12Z) - MTDNS: Moving Target Defense for Resilient DNS Infrastructure [2.8721132391618256]
DNS (Domain Name System) is one of the most critical components of the Internet.
Researchers have been constantly developing methods to detect and defend against the attacks against DNS.
Most solutions discard packets for defensive approaches, which can cause legitimate packets to be dropped.
We propose MTDNS, a resilient MTD-based approach that employs Moving Target Defense techniques.
arXiv Detail & Related papers (2024-10-03T06:47:16Z) - Exploiting Sequence Number Leakage: TCP Hijacking in NAT-Enabled Wi-Fi Networks [22.72218888270886]
We uncover a new side-channel vulnerability in the widely used NAT port preservation strategy and an insufficient reverse path validation strategy of Wi-Fi routers.
Off-path attackers can infer if there is one victim client in the same network communicating with another host on the Internet using TCP.
We test 67 widely used routers from 30 vendors and discover that 52 of them are affected by this attack.
arXiv Detail & Related papers (2024-04-06T11:59:35Z) - Putting a Padlock on Lambda -- Integrating vTPMs into AWS Firecracker [49.1574468325115]
Software services place implicit trust in the cloud provider, without an explicit trust relationship.
There is currently no cloud provider that exposes Trusted Platform Module capabilities.
We improve trust by integrating a virtual TPM device into the Firecracker, originally developed by Amazon Web Services.
arXiv Detail & Related papers (2023-10-05T13:13:55Z) - Distributed-Training-and-Execution Multi-Agent Reinforcement Learning
for Power Control in HetNet [48.96004919910818]
We propose a multi-agent deep reinforcement learning (MADRL) based power control scheme for the HetNet.
To promote cooperation among agents, we develop a penalty-based Q learning (PQL) algorithm for MADRL systems.
In this way, an agent's policy can be learned by other agents more easily, resulting in a more efficient collaboration process.
arXiv Detail & Related papers (2022-12-15T17:01:56Z) - An Adaptive Device-Edge Co-Inference Framework Based on Soft
Actor-Critic [72.35307086274912]
High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices.
We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations.
Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
arXiv Detail & Related papers (2022-01-09T09:31:50Z)
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.