Trust Anchor to Mitigate a Physical-Material-Based Denial-of-Service Attack on Electronics Hardware Post-Manufacturing
Bibliographic record
Abstract
Modern supply chains for electronics components are vast networks of companies with thousands of employees. In cyber terms, this means an attack surface of thousands of human-based attack vectors, susceptible to being compromised for insider attacks. With critical infrastructure relying on modern electronics with low turnover, a pre-compromised system (i.e., by an insider in a supplier who staged a vulnerability) can reside in the infrastructure for decades waiting for the exploit to be triggered. We report the fabrication of such a vulnerability in a Z80 microprocessor that is part of an example electronics system. The vulnerability is a physical material-based hardware modification allowing for a triggerable hardware attack and suitable for an insider with low-resource access and limited strategic planning. The modification introduces an implant into a standard dual inline package of a Z80 microprocessor. The implant consists of an NTC temperature detection in parallel with a chip resistor, that are soldered in series with the power supply to the Z80. A temperature of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$-10^{\circ} \mathrm{C}$</tex> was chosen as the limit below which the attack triggers, causing the subsequent failure of the tampered specimen. As defense we demonstrate a radio-frequency reflectometry method as a trust anchor to successfully identify tampered specimens based on significantly altered reflectometry spectra. The reported attack and defense methods show an example of moving from trust-based supply chains with sampling to a zero-trust supply chain model with advanced trust anchors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".