# Fake Resume Attacks: Data Poisoning on Online Job Platforms

arXiv: https://arxiv.org/abs/2402.14124 · HTML: https://mlsanity.com/paper/2402.14124/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Backdoor and data poisoning attacks, one of 12 directions in LLM safety and security.
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Placed in

- LLM safety and security / [Backdoor and data poisoning attacks](https://mlsanity.com/safety/backdoor-and-data-poisoning-attacks/)

## Builds on (most cited first, 17 of 17)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning](https://mlsanity.com/paper/1712.05526/) (2017)
- [Adversarial Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/1805.07984/) (2018)
- [Adversarial Attack on Graph Structured Data](https://mlsanity.com/paper/1806.02371/) (2018)
- [Adversarial Attacks and Defenses in Images, Graphs and Text: A Review](https://mlsanity.com/paper/1909.08072/) (2019)
- [Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks](https://mlsanity.com/paper/2006.12557/) (2020)
- [Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach](https://mlsanity.com/paper/1812.08947/) (2018)
- [Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction](https://mlsanity.com/paper/2109.01165/) (2021)
- [Data Poisoning Attack against Knowledge Graph Embedding](https://mlsanity.com/paper/1904.12052/) (2019)
- [Practical Data Poisoning Attack against Next-Item Recommendation](https://mlsanity.com/paper/2004.03728/) (2020)
- [Towards Deep and Representation Learning for Talent Search at LinkedIn](https://mlsanity.com/paper/1809.06473/) (2018)
- [A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics](https://mlsanity.com/paper/2307.03195/) (2023)
- [Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning](https://mlsanity.com/paper/2009.07429/) (2020)
- [Salience and Market-aware Skill Extraction for Job Targeting](https://mlsanity.com/paper/2005.13094/) (2020)
- [Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach](https://mlsanity.com/paper/1712.03087/) (2017)
- [Learning to Ask Screening Questions for Job Postings](https://mlsanity.com/paper/2004.14969/) (2020)
- [JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning](https://mlsanity.com/paper/2202.10739/) (2022)

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

- [AI Security Beyond Core Domains: Resume Screening as a Case Study of Adversarial Vulnerabilities in Specialized LLM Applications](https://mlsanity.com/paper/2512.20164/) (2025)
- [CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship](https://mlsanity.com/paper/2408.15620/) (2024)
- [Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability](https://mlsanity.com/paper/2607.10402/) (2026)

## Abstract (arXiv)

While recent studies have exposed various vulnerabilities incurred from data poisoning attacks in many web services, little is known about the vulnerability on online professional job platforms (e.g., LinkedIn and Indeed). In this work, first time, we demonstrate the critical vulnerabilities found in the common Human Resources (HR) task of matching job seekers and companies on online job platforms. Capitalizing on the unrestricted format and contents of job seekers' resumes and easy creation of accounts on job platforms, we demonstrate three attack scenarios: (1) company promotion attack to increase the likelihood of target companies being recommended, (2) company demotion attack to decrease the likelihood of target companies being recommended, and (3) user promotion attack to increase the likelihood of certain users being matched to certain companies. To this end, we develop an end-to-end "fake resume" generation framework, titled FRANCIS, that induces systematic prediction errors via data poisoning. Our empirical evaluation on real-world datasets reveals that data poisoning attacks can markedly skew the results of matchmaking between job seekers and companies, regardless of underlying models, with vulnerability amplified in proportion to poisoning intensity. These findings suggest that the outputs of various services from job platforms can be potentially hacked by malicious users.