Security and Privacy Challenges of Large Language Models: A Survey
- URL: http://arxiv.org/abs/2402.00888v1
- Date: Tue, 30 Jan 2024 04:00:54 GMT
- Title: Security and Privacy Challenges of Large Language Models: A Survey
- Authors: Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu
- Abstract summary: Large Language Models (LLMs) have demonstrated extraordinary capabilities and contributed to multiple fields, such as generating and summarizing text, language translation, and question-answering.
These models are also vulnerable to security and privacy attacks, such as jailbreaking attacks, data poisoning attacks, and Personally Identifiable Information (PII) leakage attacks.
This survey provides a thorough review of the security and privacy challenges of LLMs for both training data and users, along with the application-based risks in various domains, such as transportation, education, and healthcare.
- Score: 2.9480813253164535
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large Language Models (LLMs) have demonstrated extraordinary capabilities and
contributed to multiple fields, such as generating and summarizing text,
language translation, and question-answering. Nowadays, LLM is becoming a very
popular tool in computerized language processing tasks, with the capability to
analyze complicated linguistic patterns and provide relevant and appropriate
responses depending on the context. While offering significant advantages,
these models are also vulnerable to security and privacy attacks, such as
jailbreaking attacks, data poisoning attacks, and Personally Identifiable
Information (PII) leakage attacks. This survey provides a thorough review of
the security and privacy challenges of LLMs for both training data and users,
along with the application-based risks in various domains, such as
transportation, education, and healthcare. We assess the extent of LLM
vulnerabilities, investigate emerging security and privacy attacks for LLMs,
and review the potential defense mechanisms. Additionally, the survey outlines
existing research gaps in this domain and highlights future research
directions.
Related papers
- A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends [78.3201480023907]
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a wide range of multimodal understanding and reasoning tasks.
The vulnerability of LVLMs is relatively underexplored, posing potential security risks in daily usage.
In this paper, we provide a comprehensive review of the various forms of existing LVLM attacks.
arXiv Detail & Related papers (2024-07-10T06:57:58Z) - Unique Security and Privacy Threats of Large Language Model: A Comprehensive Survey [46.19229410404056]
Large language models (LLMs) have made remarkable advancements in natural language processing.
These models are trained on vast datasets to exhibit powerful language understanding and generation capabilities.
Privacy and security issues have been revealed throughout their life cycle.
arXiv Detail & Related papers (2024-06-12T07:55:32Z) - Large Language Models for Cyber Security: A Systematic Literature Review [14.924782327303765]
We conduct a comprehensive review of the literature on the application of Large Language Models in cybersecurity (LLM4Security)
We observe that LLMs are being applied to a wide range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection.
Third, we identify several promising techniques for adapting LLMs to specific cybersecurity domains, such as fine-tuning, transfer learning, and domain-specific pre-training.
arXiv Detail & Related papers (2024-05-08T02:09:17Z) - Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices [4.927763944523323]
Large language models (LLMs) have significantly transformed the landscape of Natural Language Processing (NLP)
This research paper thoroughly investigates security and privacy concerns related to LLMs from five thematic perspectives.
The paper recommends promising avenues for future research to enhance the security and risk management of LLMs.
arXiv Detail & Related papers (2024-03-19T07:10:58Z) - On Protecting the Data Privacy of Large Language Models (LLMs): A Survey [35.48984524483533]
Large language models (LLMs) are complex artificial intelligence systems capable of understanding, generating and translating human language.
LLMs process and generate large amounts of data, which may threaten data privacy.
arXiv Detail & Related papers (2024-03-08T08:47:48Z) - Large Language Models in Cybersecurity: State-of-the-Art [4.990712773805833]
The rise of Large Language Models (LLMs) has revolutionized our comprehension of intelligence bringing us closer to Artificial Intelligence.
This study examines the existing literature, providing a thorough characterization of both defensive and adversarial applications of LLMs within the realm of cybersecurity.
arXiv Detail & Related papers (2024-01-30T16:55:25Z) - A Survey on Detection of LLMs-Generated Content [97.87912800179531]
The ability to detect LLMs-generated content has become of paramount importance.
We aim to provide a detailed overview of existing detection strategies and benchmarks.
We also posit the necessity for a multi-faceted approach to defend against various attacks.
arXiv Detail & Related papers (2023-10-24T09:10:26Z) - Privacy in Large Language Models: Attacks, Defenses and Future
Directions [46.30861174408193]
We analyze the current privacy attacks targeting large language models (LLMs) and categorize them according to the adversary's assumed capabilities.
We present a detailed overview of prominent defense strategies that have been developed to counter these privacy attacks.
arXiv Detail & Related papers (2023-10-16T13:23:54Z) - On the Risk of Misinformation Pollution with Large Language Models [127.1107824751703]
We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation.
Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation in the performance of Open-Domain Question Answering (ODQA) systems.
arXiv Detail & Related papers (2023-05-23T04:10:26Z) - Safety Assessment of Chinese Large Language Models [51.83369778259149]
Large language models (LLMs) may generate insulting and discriminatory content, reflect incorrect social values, and may be used for malicious purposes.
To promote the deployment of safe, responsible, and ethical AI, we release SafetyPrompts including 100k augmented prompts and responses by LLMs.
arXiv Detail & Related papers (2023-04-20T16:27:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.