CRESS: Framework for Vulnerability Assessment of Attack Scenarios in Hardware Reverse Engineering
Bibliographic record
Abstract
Trust and security of microelectronic systems are a major driver for game-changing trends like autonomous driving or the internet of things. These trends are endangered by threats like soft- and hardware attacks or IP tampering – wherein often hardware reverse engineering (RE) is involved for efficient attack planning. The constant publication of new RE-related scenarios and countermeasures renders a profound rating of these extremely difficult. Researchers and practitioners have no tools or framework which aid a common, consistent classification of these scenarios. In this work, this rating framework is introduced: the common reverse engineering scoring system (CRESS). The framework allows a general classification of published settings and renders them comparable. We introduce three metrics: exploitability, impact, and a timestamp. For these metrics, attributes are defined which allow a granular assessment of RE on the one hand, and attack requirements, consequences, and potential remediation strategies on the other. The system is demonstrated in detail via five case studies and common implications are discussed. We anticipate CRESS to evaluate possible vulnerabilities and to safeguard targets more proactively.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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".