A compiler-based infrastructure for software-protection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Not long after the introduction of stored-program computing machines, the first high-level language compilers appeared. The need for automatically and efficiently mapping abstract concepts from high-level languages onto low-level assembly languages has been recognized ever since. A compiler has a unique ability to gather and analyze large amounts of data in a manner that would be an unwieldy manual endeavor. It is this property that makes known compiler techniques and technology ideally suited for the purposes of software protection against reverse engineering and tampering attacks. In this paper, we present a code transformation infrastructure combined with build-time security techniques that are used to integrate protection into otherwise vulnerable machine programs. We show the applicability of known compiler techniques such as aliasanalysis, whole program analysis, data-flow analysis, and control-flow analysis and how these capabilities provide the basis for program transformations that provide comprehensive software protection. These methods are incorporated in an extensible framework allowing efficient development of new code transformations, as part of a larger suite of security tools for the creation of robust applications. We describe a number of successful applications of these tools.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it