Towards Formal Verification of Program Obfuscation
Bibliographic record
Abstract
Code obfuscation involves transforming a program to a new version that performs the same computation but hides the functionality of the original code. An important property of such a transformation is that it preserves the behavior of the original program. In this paper, we lay the foundation for studying and reasoning about code obfuscating transformations, and show how the preservation of certain behaviours may be formally verified. To this end, we apply techniques of formal specification and verification using the Coq Proof Assistant. We use and extend an existing encoding of a simple imperative language in Coq along with an encoding of Hoare logic for reasoning about this language. We formulate what it means for a program's semantics to be preserved by an obfuscating transformation, and give formal machine-checked proofs that these behaviours or properties hold. We also define a lower-level block-structured language which is "wrapped around" our imperative language, allowing us to model certain flattening transformations and treat blocks of codes as objects in their own right.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".