RAVAGE: Robotic Autonomous Vehicles’ Attack Generation Engine
Bibliographic record
Abstract
Physical attacks such as sensor spoofing and tampering are a growing concern for Robotic Autonomous Vehicles (RAV) such as drones and rovers. Studying the impact of these attacks and developing defense techniques is challenging, as it requires sophisticated signal injection hardware. Consequently, prior work in RAV security simulates physical attacks through software by injecting bias into sensors. However, the absence of a standardized method for attack injection compels researchers to use custom approaches. This lack of uniformity leads to challenges in reproducibility and, at times, questionable claims.We present RAVAGE a tool for injecting realistic physical attacks through software. RAVAGE is easily extensible to multiple autopilot software and RAV types. It also allows users to configure the attack parameters without any code modifications. Further, RAVAGE automates the injection of both overt and stealthy attacks, offering a comprehensive setup for RAV security experiments. We evaluate RAVAGE on three virtual and three real RAVs, targeting six different types of RAV sensors, across a wide range of missions. We find that the attacks injected by RAVAGE resulted in crashes or mission failure in over 75% of the cases while incurring less than 2% performance overhead.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".