Phoenix: Surviving Unpatched Vulnerabilities via Accurate and Efficient Filtering of Syscall Sequences
Bibliographic record
Abstract
Known, but unpatched vulnerabilities represent one of the most concerning threats for businesses today.The average time-to-patch of zero-day vulnerabilities remains around 100 days in recent years.The lack of means to mitigate an unpatched vulnerability may force businesses to temporarily shut down their services, which can lead to significant financial loss.Existing solutions for filtering system calls unused by a container can effectively reduce the general attack surface, but cannot prevent a specific vulnerability that shares the same system calls with the container.On the other hand, existing provenance analysis solutions can help identify a sequence of system calls behind the vulnerability, although they do not provide a direct solution for filtering such a sequence.To bridge such a research gap, we propose Phoenix, a solution for preventing exploits of unpatched vulnerabilities by accurately and efficiently filtering sequences of system calls identified through provenance analysis.To achieve this, Phoenix cleverly combines the efficiency of Seccomp filters with the accuracy of Ptrace-based deep argument inspection, and it provides the novel capability of filtering system call sequences through a dynamic Seccomp design.Our implementation and experiments show that Phoenix can effectively mitigate real-world vulnerabilities which evade existing solutions, while introducing negligible delay (less than 4%) and less overhead (e.g., 98% less CPU consumption than existing solution).
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".