An implementation of a verification condition generator for foundational proof-carrying code
Bibliographic record
Abstract
Proof-carrying code (PCC) is a technique that addresses the problem of mobile code safety. It is a mechanism in which a code producer provides both code and a proof certifying that the code will run safely on a code consumer's machine. The code consumer or the host system will validate the proof against a safety policy before executing the source code. Foundational proof-carrying code (FPCC) aims to minimize the amount of code that must be trusted (the “trusted computing base” or TCB) with the goal of providing more flexibility and increased security. In both PCC and FPCC, the verification-condition generator (VCG) constructs the statement of the safety theorem from the source code, and is an important part of the TCB. This paper presents an implementation of a VCG based on a sound set of Hoare-style rules for machine instructions in the context of FPCC. The implementation in OCaml is described and examples illustrating the approach are given. The output of our VCG is a list of verification conditions that are directly inserted into a proof script that serves as input to the Coq proof assistant, and represents an important part of the safety proofs of our programs. We also present examples showing how these verification conditions are used to complete the proofs of safety. This work represents an important step in automating proofs for PCC.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".