# Characterizing Improper Input Validation Vulnerabilities of Mobile Crowdsourcing Services

arXiv: https://arxiv.org/abs/2110.08517 · HTML: https://mlsanity.com/paper/2110.08517/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security.
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Placed in

- LLM safety and security / [Prompt injection and agent security](https://mlsanity.com/safety/prompt-injection-and-agent-security/)

## Builds on (most cited first, 9 of 9)

- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection](https://mlsanity.com/paper/1907.09177/) (2019)
- [Error Rate Bounds and Iterative Weighted Majority Voting for Crowdsourcing](https://mlsanity.com/paper/1411.4086/) (2014)
- [Automated Crowdturfing Attacks and Defenses in Online Review Systems](https://mlsanity.com/paper/1708.08151/) (2017)
- [Stay On-Topic: Generating Context-specific Fake Restaurant Reviews](https://mlsanity.com/paper/1805.02400/) (2018)
- [Defending against Sybil Devices in Crowdsourced Mapping Services](https://mlsanity.com/paper/1508.00837/) (2015)
- [Crowdsourcing Dilemma](https://mlsanity.com/paper/1304.3548/) (2013)

## Abstract (arXiv)

Mobile crowdsourcing services (MCS), enable fast and economical data acquisition at scale and find applications in a variety of domains. Prior work has shown that Foursquare and Waze (a location-based and a navigation MCS) are vulnerable to different kinds of data poisoning attacks. Such attacks can be upsetting and even dangerous especially when they are used to inject improper inputs to mislead users. However, to date, there is no comprehensive study on the extent of improper input validation (IIV) vulnerabilities and the feasibility of their exploits in MCSs across domains. In this work, we leverage the fact that MCS interface with their participants through mobile apps to design tools and new methodologies embodied in an end-to-end feedback-driven analysis framework which we use to study 10 popular and previously unexplored services in five different domains. Using our framework we send tens of thousands of API requests with automatically generated input values to characterize their IIV attack surface. Alarmingly, we found that most of them (8/10) suffer from grave IIV vulnerabilities which allow an adversary to launch data poisoning attacks at scale: 7400 spoofed API requests were successful in faking online posts for robberies, gunshots, and other dangerous incidents, faking fitness activities with supernatural speeds and distances among many others. Lastly, we discuss easy to implement and deploy mitigation strategies which can greatly reduce the IIV attack surface and argue for their use as a necessary complementary measure working toward trustworthy mobile crowdsourcing services.