Attacking Connection Tracking Frameworks as used by Virtual Private Networks
Bibliographic record
Abstract
VPNs (Virtual Private Networks) have become an essential privacy-enhancing technology, particularly for at-risk users like dissidents, journalists, NGOs, and others vulnerable to targeted threats. While previous research investigating VPN security has focused on cryptographic strength or traffic leakages, there remains a gap in understanding how lower-level primitives fundamental to VPN operations, like connection tracking, might undermine the security and privacy that VPNs are intended to provide. In this paper, we examine the connection tracking frameworks used in common operating systems, identifying a novel exploit primitive that we refer to as the port shadow. We use the port shadow to build four attacks against VPNs that allow an attacker to intercept and redirect encrypted traffic, de-anonymize a VPN peer, or even portscan a VPN peer behind the VPN server. We build a formal model of modern connection tracking frameworks and identify that the root cause of the port shadow lies in five shared, limited resources. Through bounded model checking, we propose and verify six mitigations in terms of enforcing process isolation. We hope our work leads to more attention on the security aspects of lower-level systems and the implications of integrating them into security-critical applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".