Watch your Constants: Malicious Streebog.
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
Abstract. In August 2012, the Streebog hash function was selected as the new Russian cryptographic hash standard (GOST R 34.11-2012). In this paper, we investigate the new standard in the context of malicious hashing and present a practical collision for a malicious version of the full hash function. In particular, we apply the rebound attack to find three solutions for three different differential paths for four rounds, and using the freedom of the round constants we connect them to obtain a collision for the twelve rounds of the compression function. Additionally, and due to the simple processing of the counter, we bypass the barrier of the checksum finalization step and transfer the compression function collision to the hash function output with no additional cost. The presented attack has a practical complexity and is verified by an example. While the results of this paper may not have a direct impact on the security of the current Streebog hash function, it presents an urge for the designers to publish the origin of the used parameters and the rational behind their choices in order for this function to gain enough confidence and wide spread adoption by the security community.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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