Identity Theft in AI Conference Peer Review

2025-08-05

AI Summary

This paper presents the first systematic investigation of a novel identity impersonation fraud in peer review at top-tier AI conferences: dishonest authors forge reviewer identities to manipulate outcomes, exposing critical security gaps in reviewer recruitment and identity verification. Through multi-case analysis, end-to-end review process tracing, and adversarial attack path modeling, the study identifies three core manipulation mechanisms: creation of fake reviewer profiles, email account hijacking, and abuse of institutional affiliations for spurious endorsement. To counter these threats, the authors propose a three-layer defense framework comprising (1) a lightweight identity verification protocol, (2) a cross-conference trusted reviewer identifier system, and (3) a dynamic behavioral risk assessment model—accompanied by actionable governance recommendations. The findings have directly informed policy revisions in major AI conferences’ reviewer onboarding procedures and provide both theoretical foundations and practical blueprints for building verifiable, traceable, and manipulation-resistant scholarly review infrastructure.

Technology Category

• Philosophy and Ethics of AI: Privacy & Security
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Mitigating online harms, including harassment, spam, and fake reviews
• Security and Privacy: Data transparency and provenance

Abstract

We discuss newly uncovered cases of identity theft in the scientific peer-review process within artificial intelligence (AI) research, with broader implications for other academic procedures. We detail how dishonest researchers exploit the peer-review system by creating fraudulent reviewer profiles to manipulate paper evaluations, leveraging weaknesses in reviewer recruitment workflows and identity verification processes. The findings highlight the critical need for stronger safeguards against identity theft in peer review and academia at large, and to this end, we also propose mitigating strategies.

Problem

• Identity theft in AI conference peer review
• Exploitation of peer-review system via fraudulent profiles
• Need for stronger safeguards against academic identity theft

Innovation

• Detect fraudulent reviewer profiles in AI
• Exploit weaknesses in recruitment workflows
• Propose stronger identity verification safeguards