# A Comprehensive Overview of Backdoor Attacks in Large Language Models within Communication Networks

arXiv: https://arxiv.org/abs/2308.14367 · HTML: https://mlsanity.com/paper/2308.14367/

## Key facts

- Cited by 42 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 13 of its 15 references that are in the map.
- First posted on arXiv 28 Aug 2023.

## 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, 13 of 13)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Ignore Previous Prompt: Attack Techniques For Language Models](https://mlsanity.com/paper/2211.09527/) (2022)
- [Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning](https://mlsanity.com/paper/2108.13888/) (2021)
- [Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models](https://mlsanity.com/paper/2305.14710/) (2023)
- [Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models](https://mlsanity.com/paper/2103.15543/) (2021)
- [Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models](https://mlsanity.com/paper/2305.01219/) (2023)
- [Trojaning Language Models for Fun and Profit](https://mlsanity.com/paper/2008.00312/) (2020)
- [Hidden Backdoors in Human-Centric Language Models](https://mlsanity.com/paper/2105.00164/) (2021)
- [BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models](https://mlsanity.com/paper/2110.02467/) (2021)
- [BadPrompt: Backdoor Attacks on Continuous Prompts](https://mlsanity.com/paper/2211.14719/) (2022)
- [Adversarial Demonstration Attacks on Large Language Models](https://mlsanity.com/paper/2305.14950/) (2023)
- [Backdoor Learning on Sequence to Sequence Models](https://mlsanity.com/paper/2305.02424/) (2023)
- [Backdoor Attacks with Input-unique Triggers in NLP](https://mlsanity.com/paper/2303.14325/) (2023)

## Built on by (most cited first, 30 of 42)

- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Security and Privacy Challenges of Large Language Models: A Survey](https://mlsanity.com/paper/2402.00888/) (2024)
- [The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies](https://mlsanity.com/paper/2407.19354/) (2024)
- [Unleashing the potential of prompt engineering for large language models](https://mlsanity.com/paper/2310.14735/) (2023)
- [Backdoor Attacks and Countermeasures in Natural Language Processing Models: A Comprehensive Security Review](https://mlsanity.com/paper/2309.06055/) (2023)
- [LLM as OS, Agents as Apps: Envisioning AIOS, Agents and the AIOS-Agent Ecosystem](https://mlsanity.com/paper/2312.03815/) (2023)
- [Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices](https://mlsanity.com/paper/2403.12503/) (2024)
- [PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimization](https://mlsanity.com/paper/2504.07717/) (2025)
- [MOSSBench: Is Your Multimodal Language Model Oversensitive to Safe Queries?](https://mlsanity.com/paper/2406.17806/) (2024)
- [NeuroStrike: Neuron-Level Attacks on Aligned LLMs](https://mlsanity.com/paper/2509.11864/) (2025)
- [Machine Unlearning in Large Language Models](https://mlsanity.com/paper/2404.16841/) (2024)
- [RTL-Breaker: Assessing the Security of LLMs against Backdoor Attacks on HDL Code Generation](https://mlsanity.com/paper/2411.17569/) (2024)
- [SALAD: Systematic Assessment of Machine Unlearning on LLM-Aided Hardware Design](https://mlsanity.com/paper/2506.02089/) (2025)
- [Exploring Vulnerabilities and Protections in Large Language Models: A Survey](https://mlsanity.com/paper/2406.00240/) (2024)
- [VeriLeaky: Navigating IP Protection vs Utility in Fine-Tuning for LLM-Driven Verilog Coding](https://mlsanity.com/paper/2503.13116/) (2025)
- [Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing](https://mlsanity.com/paper/2412.13341/) (2024)
- [LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures](https://mlsanity.com/paper/2505.01177/) (2025)
- [Large Language Models meet Network Slicing Management and Orchestration](https://mlsanity.com/paper/2403.13721/) (2024)
- [Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms](https://mlsanity.com/paper/2507.06323/) (2025)
- [Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems](https://mlsanity.com/paper/2311.11796/) (2023)
- [Can LLMs be Fooled? Investigating Vulnerabilities in LLMs](https://mlsanity.com/paper/2407.20529/) (2024)
- [Neutralizing Backdoors through Information Conflicts for Large Language Models](https://mlsanity.com/paper/2411.18280/) (2024)
- [Unelicitable Backdoors in Language Models via Cryptographic Transformer Circuits](https://mlsanity.com/paper/2406.02619/) (2024)
- [Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models](https://mlsanity.com/paper/2406.05660/) (2024)
- [Self and Cross-Model Distillation for LLMs: Effective Methods for Refusal Pattern Alignment](https://mlsanity.com/paper/2406.11285/) (2024)
- [CyberLLMInstruct: A Pseudo-malicious Dataset Revealing Safety-performance Trade-offs in Cyber Security LLM Fine-tuning](https://mlsanity.com/paper/2503.09334/) (2025)
- [SoK: Semantic Privacy in Large Language Models](https://mlsanity.com/paper/2506.23603/) (2025)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [Securing Educational LLMs: A Generalised Taxonomy of Attacks on LLMs and DREAD Risk Assessment](https://mlsanity.com/paper/2508.08629/) (2025)
- [Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition](https://mlsanity.com/paper/2404.02696/) (2024)

## Abstract (arXiv)

The Large Language Models (LLMs) are poised to offer efficient and intelligent services for future mobile communication networks, owing to their exceptional capabilities in language comprehension and generation. However, the extremely high data and computational resource requirements for the performance of LLMs compel developers to resort to outsourcing training or utilizing third-party data and computing resources. These strategies may expose the model within the network to maliciously manipulated training data and processing, providing an opportunity for attackers to embed a hidden backdoor into the model, termed a backdoor attack. Backdoor attack in LLMs refers to embedding a hidden backdoor in LLMs that causes the model to perform normally on benign samples but exhibit degraded performance on poisoned ones. This issue is particularly concerning within communication networks where reliability and security are paramount. Despite the extensive research on backdoor attacks, there remains a lack of in-depth exploration specifically within the context of LLMs employed in communication networks, and a systematic review of such attacks is currently absent. In this survey, we systematically propose a taxonomy of backdoor attacks in LLMs as used in communication networks, dividing them into four major categories: input-triggered, prompt-triggered, instruction-triggered, and demonstration-triggered attacks. Furthermore, we conduct a comprehensive analysis of the benchmark datasets. Finally, we identify potential problems and open challenges, offering valuable insights into future research directions for enhancing the security and integrity of LLMs in communication networks.