Exploring QUIC Security and Privacy: A Comprehensive Survey on QUIC Security and Privacy Vulnerabilities, Threats, Attacks, and Future Research Directions
Bibliographic record
Abstract
QUIC is a modern transport protocol aiming to improve Web connection performance and security. It is the transport layer for HTTP/3. QUIC offers numerous advantages over traditional transport layer protocols, such as TCP and UDP, including reduced latency, improved congestion control, connection migration and encryption by default. However, these benefits introduce new security and privacy challenges that need to be addressed, as cyber attackers can exploit weaknesses in the protocol. QUIC’s security and privacy issues have been largely unexplored, as existing research on QUIC primarily focuses on performance upgrades. This survey paper addresses the knowledge gap in QUIC’s security and privacy challenges while proposing directions for future research to enhance its security and privacy. Our comprehensive analysis covers QUIC’s history, architecture, core mechanisms (such as cryptographic design and handshaking process), security model, and threat landscape. We examine QUIC’s significant vulnerabilities, critical security and privacy attacks, emerging threats, advanced security and privacy challenges, and mitigation strategies. Furthermore, we outline future research directions to improve QUIC’s security and privacy. By exploring the protocol’s security and privacy implications, this paper informs decision-making processes and enhances online safety for users and professionals. Our research identifies key risks, vulnerabilities, threats, and attacks targeting QUIC, providing actionable insights to strengthen the protocol. Through this comprehensive analysis, we contribute to developing and deploying a faster, more secure next-generation Internet infrastructure. We hope this investigation serves as a foundation for future Internet security and privacy innovations, ensuring robust protection for modern digital communications.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".