ChatBug: A Common Vulnerability of Aligned LLMs Induced by Chat Templates

Fengqing Jiang, Zhangchen Xu, Luyao Niu, Bill Yuchen Lin, Radha Poovendran·June 17, 2024

Summary

This collection of research papers delves into the vulnerabilities and complexities of large language models (LLMs), specifically focusing on the ChatBug vulnerability introduced by chat templates in instruction tuning. The vulnerability allows malicious users to exploit LLMs through format mismatch and message overflow attacks, bypassing safety alignment and potentially generating harmful responses. The studies demonstrate successful attacks on multiple state-of-the-art LLMs, highlighting a trade-off between safety and performance, as adversarial training can mitigate the vulnerability but negatively impacts model effectiveness. Researchers investigate countermeasures, such as Self-Reminder, SafeDecoding, and Adversarial Training, to address the issue, emphasizing the need for a balance between safety and conversational capabilities. The papers also touch upon jailbreak attacks, prompt engineering, and the importance of prompt design, as well as the challenges in auditing models for safety and evaluating their decision-making processes. The overall research aims to improve the security of LLMs while considering the implications for human interaction and collaboration.

Key findings

1

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the common vulnerability named ChatBug induced by chat templates used during instruction tuning for large language models (LLMs) . This vulnerability can be exploited by malicious users to provoke unintended behaviors from state-of-the-art aligned LLMs, potentially leading to security risks and unintended consequences . While the specific vulnerability named ChatBug is a new problem identified in the paper, the broader issue of ensuring the safety alignment of LLMs and mitigating potential risks associated with chat templates is an ongoing concern in the field of natural language processing and AI safety .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis regarding the impact of chat templates on the safety alignment of Large Language Models (LLMs) . The key hypothesis investigated is the existence of a common vulnerability named ChatBug induced by chat templates used during the fine-tuning of LLMs . The study explores how chat templates, which structure data for optimizing LLM performance, can inadvertently introduce vulnerabilities that malicious users can exploit to provoke unintended behaviors from LLMs . The research delves into the potential risks associated with chat templates in terms of safety alignment of LLMs and the effectiveness of different mitigation strategies to address the identified vulnerabilities .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper introduces a common vulnerability named ChatBug induced by chat templates used during instruction tuning. It proposes two attacks, the format mismatch attack and message overflow attack, to exploit this vulnerability . The study assesses the severity of the ChatBug vulnerability by demonstrating that malicious users can provoke unintended behaviors from state-of-the-art aligned Large Language Models (LLMs) effectively . Additionally, the paper investigates potential techniques to mitigate the ChatBug vulnerability .

In terms of new methods, the paper presents attacks such as the format mismatch attack and message overflow attack to exploit the ChatBug vulnerability in aligned LLMs . It also discusses the effectiveness of Adversarial Training in balancing safety alignment and helpfulness in LLMs, highlighting a trade-off between safety alignment and performance degradation . Furthermore, the paper explores techniques like Self-Reminder and SafeDecoding as defense mechanisms against jailbreak attacks on LLMs . The paper introduces novel attacks, such as the format mismatch attack and message overflow attack, to exploit the ChatBug vulnerability in aligned Large Language Models (LLMs) . Compared to previous methods, the study evaluates the severity of the ChatBug vulnerability and demonstrates that malicious users can effectively provoke unintended behaviors from state-of-the-art aligned LLMs . Additionally, the paper investigates potential techniques to mitigate the ChatBug vulnerability, highlighting the effectiveness of Adversarial Training in balancing safety alignment and helpfulness in LLMs .

In terms of characteristics and advantages, the paper presents a comprehensive evaluation of countermeasures to the ChatBug vulnerability, focusing on the Vicuna model as it shows the highest ASR on average . The study uses metrics like ASR and MT-Bench to assess the effectiveness of countermeasures in mitigating the ChatBug vulnerability . It is observed that while mitigation-based countermeasures like Self-Reminder and SafeDecoding fail to fully mitigate the vulnerability, Adversarial Training proves to be an effective technique, although it comes at the cost of significant performance degradation .

Furthermore, the paper emphasizes the need for developers to carefully balance the trade-off between safety alignment and helpfulness in future developments of LLMs, as indicated by the sharp drop in the MT-bench score when employing Adversarial Training . This highlights the importance of considering the impact on performance when implementing security measures to address vulnerabilities like ChatBug in aligned LLMs .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of large language models (LLMs) and chat templates. Noteworthy researchers in this field include Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Michiel Bakker, Martin Chadwick, Hannah Sheahan, Michael Tessler, Lucy Campbell-Gillingham, Jan Balaguer, Nat McAleese, Amelia Glaese, John Aslanides, Matt Botvinick, Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, Eric Wong, Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, Andy Zou, Zifan Wang, J Zico Kolter, Matt Fredrikson, Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, Weiyan Shi, Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, Tong Sun, among others .

The key solution mentioned in the paper is the identification of a common vulnerability named ChatBug induced by chat templates used during instruction tuning for LLMs. This vulnerability arises from the rigid format provided by chat templates that need to be followed by LLMs but not necessarily by users. Malicious users can exploit this vulnerability by crafting prompts that bypass safety alignments of LLMs. The paper presents two attacks, format mismatch attack, and message overflow attack, to exploit the ChatBug vulnerability .


How were the experiments in the paper designed?

The experiments in the paper were designed with specific setups for Jailbreak Attack and Defense .

  • Jailbreak Attack Setup: Various techniques were employed such as GCG, GPTFuzzer, and ArtPrompt to carry out attacks by appending harmful instructions or using jailbreak prompts .
  • Defense Setup: Defense mechanisms like Self-Reminder, SafeDecoding, and Adversarial Training were implemented to mitigate the vulnerabilities in the victim LLMs .
  • Examples of Attacks: The experiments included examples of attacks such as format mismatch attack and message overflow attack to exploit the ChatBug vulnerability .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the Vicuna model . The code for the Vicuna model is open-source and can be accessed for further exploration and research .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The paper identifies a common vulnerability called ChatBug induced by chat templates used during instruction tuning and develops two attacks, format mismatch attack, and message overflow attack, to exploit this vulnerability . The severity of the ChatBug vulnerability is demonstrated by showing how malicious users can effectively provoke unintended behaviors from state-of-the-art aligned Large Language Models (LLMs) . Additionally, the paper highlights that jailbreak attacks can significantly increase their success rates by exploiting the ChatBug vulnerability .

The experimental results in the paper indicate that mitigation-based countermeasures, such as Self-Reminder and SafeDecoding, fail to effectively mitigate the ChatBug vulnerability. While these countermeasures can defend against certain attacks, they lead to a notable degradation in performance on multi-turn conversation and instruction following abilities, as indicated by the MT-Bench scores . On the other hand, Adversarial Training is shown to be an effective countermeasure against the ChatBug vulnerability, although it comes at the cost of performance degradation . The results suggest that developers must carefully balance safety alignment and helpfulness in future LLM developments .

Overall, the experiments conducted in the paper provide strong empirical evidence supporting the scientific hypotheses related to the ChatBug vulnerability and the effectiveness of different countermeasures in mitigating this vulnerability. The results offer valuable insights into the challenges and trade-offs involved in ensuring the security and performance of aligned LLMs in the context of chat templates and instruction tuning .


What are the contributions of this paper?

The paper "ChatBug: A Common Vulnerability of Aligned LLMs Induced by Chat Templates" makes several key contributions:

  • It identifies a common vulnerability named ChatBug induced by chat templates used during instruction tuning, leading to two specific attacks: format mismatch attack and message overflow attack .
  • The severity of the ChatBug vulnerability is assessed by demonstrating how malicious users can provoke unintended behaviors from eight state-of-the-art aligned Large Language Models (LLMs) and how jailbreak attacks can exploit this vulnerability to increase attack success rates .
  • The paper explores potential techniques to mitigate the ChatBug vulnerability, highlighting the importance of balancing the trade-off between safety alignment and helpfulness in the development of LLMs .
  • It investigates how chat templates impact the safety alignment of LLMs, emphasizing the need to understand the impact of these templates on deploying LLMs safely at scale .
  • The research demonstrates that adversarial training can effectively mitigate the ChatBug vulnerability but at the cost of significant performance degradation in the victim model, underscoring the challenge of balancing safety alignment and helpfulness in LLM development .

What work can be continued in depth?

Further research in the field can focus on exploring the impact of chat templates on the safety alignment of Large Language Models (LLMs) in more detail. The study could delve into how these templates introduce vulnerabilities like ChatBug to LLMs that have been fine-tuned using such templates . Investigating the specific mechanisms through which chat templates affect the safety alignment of LLMs and how malicious users could exploit these vulnerabilities would be a valuable area for future exploration. Additionally, examining the effectiveness of different countermeasures, such as detection-based and mitigation-based approaches, in addressing vulnerabilities induced by chat templates could be a fruitful direction for continued research .

Tables

1

Introduction
Background
Evolution of large language models
Importance of instruction tuning and chat templates
Emergence of the ChatBug vulnerability
Objective
To understand the ChatBug vulnerability
To assess the impact on safety and performance
To explore countermeasures and their trade-offs
Methodology
Data Collection
Case studies on affected LLMs
Analysis of attack vectors and success rates
Public datasets and benchmarking
Data Preprocessing
Cleaning and preprocessing of attack scenarios
Identifying format mismatch and message overflow patterns
Collection of adversarial examples
Vulnerability Analysis
Format and message manipulation techniques
Safety alignment bypass mechanisms
Impact on response generation
Countermeasures and Defense Strategies
Self-Reminder
Design and implementation
Effectiveness in mitigating attacks
Performance implications
SafeDecoding
Algorithmic approach
Trade-offs with natural language generation
Real-world deployment scenarios
Adversarial Training
Training methodologies
Improved safety vs. reduced effectiveness
Current state-of-the-art approaches
Jailbreak Attacks and Prompt Engineering
Exploring model exploitation techniques
Prompt design principles for security
Limitations and challenges
Model Auditing and Transparency
Assessing safety through auditing methods
Measuring decision-making processes
Importance of explainability
Human Interaction and Collaboration
Ethical considerations for LLMs
Impact on user trust and collaboration
Future directions for safer human-LLM interactions
Conclusion
Summary of findings and implications
Open research questions and challenges
Recommendations for LLM developers and users
Basic info
papers
cryptography and security
machine learning
artificial intelligence
Advanced features
Insights
What are the potential consequences of unmitigated attacks on LLMs mentioned in the studies?
What are the countermeasures discussed by researchers to address the vulnerabilities in LLMs?
How do chat templates contribute to the ChatBug vulnerability in LLMs?
What type of vulnerabilities are explored in the research papers?

ChatBug: A Common Vulnerability of Aligned LLMs Induced by Chat Templates

Fengqing Jiang, Zhangchen Xu, Luyao Niu, Bill Yuchen Lin, Radha Poovendran·June 17, 2024

Summary

This collection of research papers delves into the vulnerabilities and complexities of large language models (LLMs), specifically focusing on the ChatBug vulnerability introduced by chat templates in instruction tuning. The vulnerability allows malicious users to exploit LLMs through format mismatch and message overflow attacks, bypassing safety alignment and potentially generating harmful responses. The studies demonstrate successful attacks on multiple state-of-the-art LLMs, highlighting a trade-off between safety and performance, as adversarial training can mitigate the vulnerability but negatively impacts model effectiveness. Researchers investigate countermeasures, such as Self-Reminder, SafeDecoding, and Adversarial Training, to address the issue, emphasizing the need for a balance between safety and conversational capabilities. The papers also touch upon jailbreak attacks, prompt engineering, and the importance of prompt design, as well as the challenges in auditing models for safety and evaluating their decision-making processes. The overall research aims to improve the security of LLMs while considering the implications for human interaction and collaboration.
Mind map
Importance of explainability
Measuring decision-making processes
Assessing safety through auditing methods
Current state-of-the-art approaches
Improved safety vs. reduced effectiveness
Training methodologies
Real-world deployment scenarios
Trade-offs with natural language generation
Algorithmic approach
Performance implications
Effectiveness in mitigating attacks
Design and implementation
Impact on response generation
Safety alignment bypass mechanisms
Format and message manipulation techniques
Collection of adversarial examples
Identifying format mismatch and message overflow patterns
Cleaning and preprocessing of attack scenarios
Public datasets and benchmarking
Analysis of attack vectors and success rates
Case studies on affected LLMs
To explore countermeasures and their trade-offs
To assess the impact on safety and performance
To understand the ChatBug vulnerability
Emergence of the ChatBug vulnerability
Importance of instruction tuning and chat templates
Evolution of large language models
Recommendations for LLM developers and users
Open research questions and challenges
Summary of findings and implications
Future directions for safer human-LLM interactions
Impact on user trust and collaboration
Ethical considerations for LLMs
Model Auditing and Transparency
Adversarial Training
SafeDecoding
Self-Reminder
Vulnerability Analysis
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Human Interaction and Collaboration
Jailbreak Attacks and Prompt Engineering
Countermeasures and Defense Strategies
Methodology
Introduction
Outline
Introduction
Background
Evolution of large language models
Importance of instruction tuning and chat templates
Emergence of the ChatBug vulnerability
Objective
To understand the ChatBug vulnerability
To assess the impact on safety and performance
To explore countermeasures and their trade-offs
Methodology
Data Collection
Case studies on affected LLMs
Analysis of attack vectors and success rates
Public datasets and benchmarking
Data Preprocessing
Cleaning and preprocessing of attack scenarios
Identifying format mismatch and message overflow patterns
Collection of adversarial examples
Vulnerability Analysis
Format and message manipulation techniques
Safety alignment bypass mechanisms
Impact on response generation
Countermeasures and Defense Strategies
Self-Reminder
Design and implementation
Effectiveness in mitigating attacks
Performance implications
SafeDecoding
Algorithmic approach
Trade-offs with natural language generation
Real-world deployment scenarios
Adversarial Training
Training methodologies
Improved safety vs. reduced effectiveness
Current state-of-the-art approaches
Jailbreak Attacks and Prompt Engineering
Exploring model exploitation techniques
Prompt design principles for security
Limitations and challenges
Model Auditing and Transparency
Assessing safety through auditing methods
Measuring decision-making processes
Importance of explainability
Human Interaction and Collaboration
Ethical considerations for LLMs
Impact on user trust and collaboration
Future directions for safer human-LLM interactions
Conclusion
Summary of findings and implications
Open research questions and challenges
Recommendations for LLM developers and users
Key findings
1

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the common vulnerability named ChatBug induced by chat templates used during instruction tuning for large language models (LLMs) . This vulnerability can be exploited by malicious users to provoke unintended behaviors from state-of-the-art aligned LLMs, potentially leading to security risks and unintended consequences . While the specific vulnerability named ChatBug is a new problem identified in the paper, the broader issue of ensuring the safety alignment of LLMs and mitigating potential risks associated with chat templates is an ongoing concern in the field of natural language processing and AI safety .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis regarding the impact of chat templates on the safety alignment of Large Language Models (LLMs) . The key hypothesis investigated is the existence of a common vulnerability named ChatBug induced by chat templates used during the fine-tuning of LLMs . The study explores how chat templates, which structure data for optimizing LLM performance, can inadvertently introduce vulnerabilities that malicious users can exploit to provoke unintended behaviors from LLMs . The research delves into the potential risks associated with chat templates in terms of safety alignment of LLMs and the effectiveness of different mitigation strategies to address the identified vulnerabilities .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper introduces a common vulnerability named ChatBug induced by chat templates used during instruction tuning. It proposes two attacks, the format mismatch attack and message overflow attack, to exploit this vulnerability . The study assesses the severity of the ChatBug vulnerability by demonstrating that malicious users can provoke unintended behaviors from state-of-the-art aligned Large Language Models (LLMs) effectively . Additionally, the paper investigates potential techniques to mitigate the ChatBug vulnerability .

In terms of new methods, the paper presents attacks such as the format mismatch attack and message overflow attack to exploit the ChatBug vulnerability in aligned LLMs . It also discusses the effectiveness of Adversarial Training in balancing safety alignment and helpfulness in LLMs, highlighting a trade-off between safety alignment and performance degradation . Furthermore, the paper explores techniques like Self-Reminder and SafeDecoding as defense mechanisms against jailbreak attacks on LLMs . The paper introduces novel attacks, such as the format mismatch attack and message overflow attack, to exploit the ChatBug vulnerability in aligned Large Language Models (LLMs) . Compared to previous methods, the study evaluates the severity of the ChatBug vulnerability and demonstrates that malicious users can effectively provoke unintended behaviors from state-of-the-art aligned LLMs . Additionally, the paper investigates potential techniques to mitigate the ChatBug vulnerability, highlighting the effectiveness of Adversarial Training in balancing safety alignment and helpfulness in LLMs .

In terms of characteristics and advantages, the paper presents a comprehensive evaluation of countermeasures to the ChatBug vulnerability, focusing on the Vicuna model as it shows the highest ASR on average . The study uses metrics like ASR and MT-Bench to assess the effectiveness of countermeasures in mitigating the ChatBug vulnerability . It is observed that while mitigation-based countermeasures like Self-Reminder and SafeDecoding fail to fully mitigate the vulnerability, Adversarial Training proves to be an effective technique, although it comes at the cost of significant performance degradation .

Furthermore, the paper emphasizes the need for developers to carefully balance the trade-off between safety alignment and helpfulness in future developments of LLMs, as indicated by the sharp drop in the MT-bench score when employing Adversarial Training . This highlights the importance of considering the impact on performance when implementing security measures to address vulnerabilities like ChatBug in aligned LLMs .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of large language models (LLMs) and chat templates. Noteworthy researchers in this field include Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Michiel Bakker, Martin Chadwick, Hannah Sheahan, Michael Tessler, Lucy Campbell-Gillingham, Jan Balaguer, Nat McAleese, Amelia Glaese, John Aslanides, Matt Botvinick, Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, Eric Wong, Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, Andy Zou, Zifan Wang, J Zico Kolter, Matt Fredrikson, Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, Weiyan Shi, Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, Tong Sun, among others .

The key solution mentioned in the paper is the identification of a common vulnerability named ChatBug induced by chat templates used during instruction tuning for LLMs. This vulnerability arises from the rigid format provided by chat templates that need to be followed by LLMs but not necessarily by users. Malicious users can exploit this vulnerability by crafting prompts that bypass safety alignments of LLMs. The paper presents two attacks, format mismatch attack, and message overflow attack, to exploit the ChatBug vulnerability .


How were the experiments in the paper designed?

The experiments in the paper were designed with specific setups for Jailbreak Attack and Defense .

  • Jailbreak Attack Setup: Various techniques were employed such as GCG, GPTFuzzer, and ArtPrompt to carry out attacks by appending harmful instructions or using jailbreak prompts .
  • Defense Setup: Defense mechanisms like Self-Reminder, SafeDecoding, and Adversarial Training were implemented to mitigate the vulnerabilities in the victim LLMs .
  • Examples of Attacks: The experiments included examples of attacks such as format mismatch attack and message overflow attack to exploit the ChatBug vulnerability .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the Vicuna model . The code for the Vicuna model is open-source and can be accessed for further exploration and research .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The paper identifies a common vulnerability called ChatBug induced by chat templates used during instruction tuning and develops two attacks, format mismatch attack, and message overflow attack, to exploit this vulnerability . The severity of the ChatBug vulnerability is demonstrated by showing how malicious users can effectively provoke unintended behaviors from state-of-the-art aligned Large Language Models (LLMs) . Additionally, the paper highlights that jailbreak attacks can significantly increase their success rates by exploiting the ChatBug vulnerability .

The experimental results in the paper indicate that mitigation-based countermeasures, such as Self-Reminder and SafeDecoding, fail to effectively mitigate the ChatBug vulnerability. While these countermeasures can defend against certain attacks, they lead to a notable degradation in performance on multi-turn conversation and instruction following abilities, as indicated by the MT-Bench scores . On the other hand, Adversarial Training is shown to be an effective countermeasure against the ChatBug vulnerability, although it comes at the cost of performance degradation . The results suggest that developers must carefully balance safety alignment and helpfulness in future LLM developments .

Overall, the experiments conducted in the paper provide strong empirical evidence supporting the scientific hypotheses related to the ChatBug vulnerability and the effectiveness of different countermeasures in mitigating this vulnerability. The results offer valuable insights into the challenges and trade-offs involved in ensuring the security and performance of aligned LLMs in the context of chat templates and instruction tuning .


What are the contributions of this paper?

The paper "ChatBug: A Common Vulnerability of Aligned LLMs Induced by Chat Templates" makes several key contributions:

  • It identifies a common vulnerability named ChatBug induced by chat templates used during instruction tuning, leading to two specific attacks: format mismatch attack and message overflow attack .
  • The severity of the ChatBug vulnerability is assessed by demonstrating how malicious users can provoke unintended behaviors from eight state-of-the-art aligned Large Language Models (LLMs) and how jailbreak attacks can exploit this vulnerability to increase attack success rates .
  • The paper explores potential techniques to mitigate the ChatBug vulnerability, highlighting the importance of balancing the trade-off between safety alignment and helpfulness in the development of LLMs .
  • It investigates how chat templates impact the safety alignment of LLMs, emphasizing the need to understand the impact of these templates on deploying LLMs safely at scale .
  • The research demonstrates that adversarial training can effectively mitigate the ChatBug vulnerability but at the cost of significant performance degradation in the victim model, underscoring the challenge of balancing safety alignment and helpfulness in LLM development .

What work can be continued in depth?

Further research in the field can focus on exploring the impact of chat templates on the safety alignment of Large Language Models (LLMs) in more detail. The study could delve into how these templates introduce vulnerabilities like ChatBug to LLMs that have been fine-tuned using such templates . Investigating the specific mechanisms through which chat templates affect the safety alignment of LLMs and how malicious users could exploit these vulnerabilities would be a valuable area for future exploration. Additionally, examining the effectiveness of different countermeasures, such as detection-based and mitigation-based approaches, in addressing vulnerabilities induced by chat templates could be a fruitful direction for continued research .

Tables
1
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