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This review systematically categorizes backdoor attacks on language models (LMs) based on attacker capabilities and attack surfaces. It also analyzes and compares emerging countermeasures, highlighting future research directions for more robust defenses.

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Area of Science:

Natural Language Processing (NLP)
Machine Learning Security
Artificial Intelligence

Background:

Language models (LMs) are increasingly used in real-world applications.
Outsourcing training and data hosting to third parties creates vulnerabilities for backdoor attacks.
Backdoor attacks pose a significant threat by enabling malicious behavior triggered by specific inputs.

Purpose of the Study:

To provide a systematic and comprehensive review of backdoor attacks on LMs.
To analyze attacker capabilities and purposes across different backdoor attack surfaces.
To offer a timely review of emerging backdoor countermeasures for the NLP community.

Main Methods:

Formalized attack surfaces into four categorizations: Attacking the Pretrained Model with Fine-tuning (APMF), Parameter-Efficient Fine-tuning (PEFT), Attacking the Final Model with Training (AFMT), and Attacking Large Language Models (ALLM).
Categorized countermeasures into two main classes: sample inspection and model inspection.
Reviewed and analyzed the advantages and disadvantages of various countermeasures, summarized benchmark datasets, and provided comparable evaluations.

Main Results:

Attacks were systematically combed under each of the four identified categorizations.
Countermeasures were reviewed, and their strengths and weaknesses were analyzed.
Benchmark datasets were summarized, and evaluations for representative attacks and defenses were presented.

Conclusions:

Identified crucial areas for future research in backdoor defense for LMs.
Emphasized the need for more efficient and practical countermeasures against backdoor attacks.
Provided a foundational review to guide researchers in understanding and mitigating backdoor threats in NLP.