Layout-Only Hardware Trojans: Attack Vectors and a Non-Golden Model Reverse Engineering-Based Counterstrategy
Bibliographic record
Abstract
Globally distributed microelectronic supply chains have disrupted trust in silicon hardware and have drawn academia’s attention toward different scenarios of malicious circuit modifications, i.e., hardware Trojans. This dynamic hardware environment, including open-source approaches and evermore outsourcing, requires constant reassessment of offensive and defensive aspects. Based on an untrusted foundry model, this work assesses the concrete technical realizations of layout-only modifications via design file editing, mask editing, or in-line alterations. Furthermore, the attack possibility on different modules within a system on a chip is qualitatively evaluated. Consequently, a modification is demonstrated on an SRAM-’PUF’ module. To link the attack point-of-view with a defensive measure, we propose a hardware reverse engineering-based countermeasure, which is non-reliant on a golden layout. Through a novel approach relying on inherent polygon properties, potentially occurring modifications are detected via clustering and a statistical evaluation of the intra-cluster distributions. Finally, the approach is demonstrated on samples from 7 nm to 150 nm, for which a modification detection rate between 95% and 100% is reached for all evaluated samples.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".