Bibliographic record
Abstract
Each year at the CanSecWest security conference in Vancouver, British Columbia, security company Tipping Point sponsors a competition that pits security researchers against each other in a bid to hack some of the most popular software and hardware products. The 2010 competition yielded some interesting results. Firefox, running on Windows 7, was hacked and forced to run an executable program. Internet Explorer 8 also fell victim to an attack, and Apple's iPhone and MacBook Pro were compromised. Aaron Portnoy of Tipping Point was monitoring the contest to see how things unfolded, and found some lessons in this year's contest. The hacks tell us some useful things about the broader security landscape, and the threats facing today's software and hardware customers. In this article, he details some of the key things that we can take away from the proceedings. We have just wrapped up this year's Pwn2Own contest, an annual event held at CanSecWest, where security researchers compete to find bugs in common browsers and smartphones. This year's contest included several impressive exploits against targets including Internet Explorer 8, Safari, Firefox and the iPhone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.030 |
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".