Invisible Adversaries: A Systematic Study of Session Manipulation Attacks on VPNs
- URL: http://arxiv.org/abs/2604.04099v1
- Date: Sun, 05 Apr 2026 12:43:08 GMT
- Title: Invisible Adversaries: A Systematic Study of Session Manipulation Attacks on VPNs
- Abstract summary: 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.
- Score: 29.461096043276104
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Virtual Private Networks (VPNs) are widely used for censorship evasion and traffic protection. VPN users expect to be provided with adequate security protection, and at the same time not be affected by other users connected to the same VPN server, which can be illustrated as the non-interference property. However, in this paper, we have identified several vulnerabilities that violate this property, specifically within the connection tracking frameworks of VPN servers, stemming from shared resource misuse and insufficient validation of session state transitions. We present three session manipulation attacks targeting TCP and UDP traffic tunneled through VPNs. 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. We evaluate these attacks against five popular connection tracking frameworks across different OSes and nine major commercial VPN providers. Experimental results reveal that all frameworks and eight providers are vulnerable to at least one of the attacks. We have responsibly disclosed our findings with countermeasures, resulting in 19 assigned CVEs/CNVDs and acknowledgments from the communities and providers.
Related papers
- Stealing Reasoning Traces from Proprietary LLM APIs [84.58379404903394]
We develop a scalable decryption jailbreak for encrypted reasoning traces.<n>It circumvents anti-distillation mechanisms, allowing adversaries to extract a proprietary model's reasoning.<n>It inadvertently reveals hazardous information hidden within the reasoning process.
arXiv Detail & Related papers (2026-08-10T17:24:50Z) - ShieldShare: Building a VPN-backed Android Hotspot for Secure Internet Sharing with Per-User Traffic Accounting [1.3318960669670048]
ShieldShare is a proxy-based Android application that enables secure VPN-backed hotspot sharing with per-user traffic accounting.<n>This enables affordable, community-controlled secure access in censored or high-surveillance environments.
arXiv Detail & Related papers (2026-05-02T18:40:37Z) - 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) - ChamaleoNet: Programmable Passive Probe for Enhanced Visibility on Erroneous Traffic [48.87214752144106]
ChamaleoNet transforms any production network into a transparent monitor to let administrators collect unsolicited and erroneous traffic directed to hosts.<n>ChamaleoNet is programmed to ignore well-formed traffic and collect only erroneous packets.<n>Simple analytics unveil internal and infected hosts, identify temporary failures, and enhance visibility on external radiation produced by attackers looking for vulnerable services.
arXiv Detail & Related papers (2025-08-17T20:54:41Z) - 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) - Efficacy of Full-Packet Encryption in Mitigating Protocol Detection for Evasive Virtual Private Networks [0.0]
Full-packet encryption is a technique used by modern evasive Virtual Private Networks (VPNs) to avoid protocol-based flagging from censorship models by disguising their traffic as random noise on the network.<n>I tested several machine learning-based classification models against the Aggressive Circumvention of Censorship (ACC) protocol, a fully-encrypted evasive VPN protocol.
arXiv Detail & Related papers (2024-12-23T07:24:36Z) - Bad Neighbors: On Understanding VPN Provider Networks [18.382471188948283]
We conduct a large-scale analysis of VPN providers and their thousands of VPN endpoints.
Our results indicate a widespread lack of traffic filtering towards internally routable networks on the majority of tested VPN service providers.
We have disclosed our findings to the affected providers and other stakeholders, and offered guidance to improve the situation.
arXiv Detail & Related papers (2024-10-11T11:51:20Z) - Secure Aggregation is Not Private Against Membership Inference Attacks [66.59892736942953]
We investigate the privacy implications of SecAgg in federated learning.
We show that SecAgg offers weak privacy against membership inference attacks even in a single training round.
Our findings underscore the imperative for additional privacy-enhancing mechanisms, such as noise injection.
arXiv Detail & Related papers (2024-03-26T15:07:58Z) - OpenVPN is Open to VPN Fingerprinting [10.58132231462485]
VPN adoption has seen steady growth over the past decade due to increased public awareness of privacy and surveillance threats.
Certain governments are attempting to restrict VPN access by identifying connections using "dual use" technology.
We develop mechanisms for accurately fingerprinting connections using DPI, the most popular protocol for commercial VPN services.
arXiv Detail & Related papers (2024-03-06T19:15:02Z) - "I just hated it and I want my money back": Data-driven Understanding of Mobile VPN Service Switching Preferences in The Wild [5.998704044356281]
We analyzed over 1.3 million reviews from 20 leading VPN apps, identifying 1,305 explicit mentions and intents to switch.
Our NLP-based analysis unveiled distinct clusters of factors motivating users to switch.
An examination of 376 blogs from six popular VPN recommendation sites revealed biases in the content.
arXiv Detail & Related papers (2024-03-04T00:02:46Z) - Practical Detection of Trojan Neural Networks: Data-Limited and
Data-Free Cases [87.69818690239627]
We study the problem of the Trojan network (TrojanNet) detection in the data-scarce regime.
We propose a data-limited TrojanNet detector (TND), when only a few data samples are available for TrojanNet detection.
In addition, we propose a data-free TND, which can detect a TrojanNet without accessing any data samples.
arXiv Detail & Related papers (2020-07-31T02:00:38Z)
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.