# Claim-Guided Textual Backdoor Attack for Practical Applications

arXiv: https://arxiv.org/abs/2409.16618 · HTML: https://mlsanity.com/paper/2409.16618/

## 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 3 of its 41 references that are in the map.
- First posted on arXiv 25 Sep 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, 3 of 3)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Obliviate: Neutralizing Task-agnostic Backdoors within the Parameter-efficient Fine-tuning Paradigm](https://mlsanity.com/paper/2409.14119/) (2024)

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

- [When LLMs Go Online: The Emerging Threat of Web-Enabled LLMs](https://mlsanity.com/paper/2410.14569/) (2024)
- [SteganoBackdoor: Evading Data-Poisoning Defenses via Steganographic Backdoors](https://mlsanity.com/paper/2511.14301/) (2025)
- [OPBackdoor: Opportunistic Backdoors via Alibi-Aligned Reasoning](https://mlsanity.com/paper/2609.24826/) (2026)

## Abstract (arXiv)

Recent advances in natural language processing and the increased use of large language models have exposed new security vulnerabilities, such as backdoor attacks. Previous backdoor attacks require input manipulation after model distribution to activate the backdoor, posing limitations in real-world applicability. Addressing this gap, we introduce a novel Claim-Guided Backdoor Attack (CGBA), which eliminates the need for such manipulations by utilizing inherent textual claims as triggers. CGBA leverages claim extraction, clustering, and targeted training to trick models to misbehave on targeted claims without affecting their performance on clean data. CGBA demonstrates its effectiveness and stealthiness across various datasets and models, significantly enhancing the feasibility of practical backdoor attacks. Our code and data will be available at https://github.com/PaperCGBA/CGBA.