Securing the Loop: A Risk Assessment Framework for Human-in-the-Loop Vulnerabilities Across SAE Levels of Autonomy
Bibliographic record
Abstract
Human-in-the-Loop (HITL) systems, particularly in autonomous driving, represent a complex socio-technical partnership. While extensive research has focused on securing either the vehicle's AI or protecting the human from external cyber threats, the vulnerabilities emerging from the interaction between the human and the machine remain critically underexplored. This paper introduces a novel risk assessment framework HITL-IT which extends the STRIDE threat model by integrating a Human Factor Multiplier (HFM) to quantify the impact of cognitive vulnerabilities. Our framework identifies that at SAE Level 3, cognitive exploits such as trust manipulation and situation awareness degradation pose the greatest risk, with risk scores significantly exceeding purely technical threats. The paper also applies the HITL-IT model to real-world incidents and presents a comparative analysis with existing frameworks. Finally, we propose a forward-looking research agenda for securing socio-technical autonomy.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".