# Exploiting Social Navigation

arXiv: https://arxiv.org/abs/1410.0151 · HTML: https://mlsanity.com/paper/1410.0151/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2014.

## Built on by (most cited first, 3 of 3)

- [Algorithmic Collective Action in Machine Learning](https://mlsanity.com/paper/2302.04262/) (2023)
- [Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses](https://mlsanity.com/paper/2104.01789/) (2021)
- [A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles](https://mlsanity.com/paper/2411.13778/) (2024)

## Abstract (arXiv)

We present an effective Sybil attack against social location based services. Our attack is based on creating a large number of reputed "bot drivers", and controlling their reported locations using fake GPS reports. We show how this attack can be used to influence social navigation systems by applying it to Waze - a prominent social navigation application used by over 50 million drivers. We show that our attack can fake traffic jams and dramatically influence routing decisions. We present several techniques for preventing the attack, and show that effective mitigation likely requires the use of additional carrier information.