CogniCrypt: Supporting developers in using cryptography
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
Previous research suggests that developers often struggle using low-level cryptographic APIs and, as a result, produce insecure code. When asked, developers desire, among other things, more tool support to help them use such APIs. In this paper, we present CogniCrypt, a tool that supports developers with the use of cryptographic APIs. CogniCrypt assists the developer in two ways. First, for a number of common cryptographic tasks, CogniCrypt generates code that implements the respective task in a secure manner. Currently, CogniCrypt supports tasks such as data encryption, communication over secure channels, and long-term archiving. Second, CogniCrypt continuously runs static analyses in the background to ensure a secure integration of the generated code into the developer's workspace. This video demo showcases the main features of CogniCrypt: youtube.com/watch?v=JUq5mRHfAWY.
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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 | 0.001 | 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