Risks associated with USB Hardware Trojan devices used by insiders
Bibliographic record
Abstract
This paper extends the discussion of potential damage that can be done by Hardware Trojan Horse devices by discussing the specific risks associated with an Insider's use of such a device to circumvent established security policies, even when these are implemented with state of the art Endpoint Security Solutions. The paper argues that a specific category of Hardware Trojan Horse devices, those implemented as functional peripheral devices, are particularly dangerous when used by a malicious Insider. The research discusses the implementation of a proof of concept Hardware Trojan Horse device, implemented as a USB Human Interface Devices, that exploits unintended USB channels to exfiltrate data from a computer. The work discusses unintended USB channels, paying particular attention to the observability of the channel in operation. Various scenarios are presented to show that Hardware Trojan Horse devices implemented as peripheral devices can be used to prosecute a wide variety of attacks that are not mitigated by modern defensive techniques. The work demonstrates that a Hardware Trojan Horse device and physical access by a malicious Insider are sufficient to compromise a modern computer system. The paper argues that the study of Hardware Trojan devices must become an integral part of research on Insider Threats.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".