Exploiting Congestion Control Parameter Manipulation in QUIC for Security Implications
Bibliographic record
Abstract
QUIC has emerged as a fundamental transport protocol for modern Internet infrastructure, serving as the foundation for HTTP/3. Although QUIC implements congestion control algorithms ($C C A$) to ensure fair network resource allocation, its user-space implementation architecture creates significant security vulnerabilities through accessible parameter manipulation. As transport layers become increasingly programmable, these vulnerabilities represent a broader security challenge for future network infrastructures where applications may deploy custom transport implementations. This paper presents a systematic analysis of selfish behaviors in QUIC through deliberate congestion control parameter (CCPM). Using the aioquic implementation, we experimentally demonstrate how strategic parameter manipulation in both NewReno and CUBIC algorithms provides substantial unfair bandwidth ($\boldsymbol{B} \boldsymbol{W}$) advantages. NewReno exhibits a major vulnerability with Loss Reduction Factor (LRF) and Congestion Avoidance Growth Rate (CAGR) manipulation, while CUBIC demonstrates better resilience, but remains exploitable, with combined LRF ($\beta_{\text {cubic }}$) and Maximum Idle Time (MIT) manipulations.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".