A Survey on Adversarial Machine Learning for Code Data: Realistic Threats, Countermeasures, and Interpretations

• URL: http://arxiv.org/abs/2411.07597v1
• Date: Tue, 12 Nov 2024 07:16:20 GMT
• Title: A Survey on Adversarial Machine Learning for Code Data: Realistic Threats, Countermeasures, and Interpretations
• Authors: Yulong Yang, Haoran Fan, Chenhao Lin, Qian Li, Zhengyu Zhao, Chao Shen, Xiaohong Guan,
• Abstract summary: Code Language Models (CLMs) have achieved tremendous progress in source code understanding and generation. In realistic scenarios, CLMs are exposed to potential malicious adversaries, bringing risks to the confidentiality, integrity, and availability of CLM systems. Despite these risks, a comprehensive analysis of the security vulnerabilities of CLMs in the extremely adversarial environment has been lacking.
• Score: 21.855757118482995
• License:
• Abstract: Code Language Models (CLMs) have achieved tremendous progress in source code understanding and generation, leading to a significant increase in research interests focused on applying CLMs to real-world software engineering tasks in recent years. However, in realistic scenarios, CLMs are exposed to potential malicious adversaries, bringing risks to the confidentiality, integrity, and availability of CLM systems. Despite these risks, a comprehensive analysis of the security vulnerabilities of CLMs in the extremely adversarial environment has been lacking. To close this research gap, we categorize existing attack techniques into three types based on the CIA triad: poisoning attacks (integrity \& availability infringement), evasion attacks (integrity infringement), and privacy attacks (confidentiality infringement). We have collected so far the most comprehensive (79) papers related to adversarial machine learning for CLM from the research fields of artificial intelligence, computer security, and software engineering. Our analysis covers each type of risk, examining threat model categorization, attack techniques, and countermeasures, while also introducing novel perspectives on eXplainable AI (XAI) and exploring the interconnections between different risks. Finally, we identify current challenges and future research opportunities. This study aims to provide a comprehensive roadmap for both researchers and practitioners and pave the way towards more reliable CLMs for practical applications.

Related papers

• Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents [67.07177243654485]
  This survey collects and analyzes the different threats faced by large language models-based agents. We identify six key features of LLM-based agents, based on which we summarize the current research progress. We select four representative agents as case studies to analyze the risks they may face in practical use.
  arXiv  Detail & Related papers  (2024-11-14T15:40:04Z)
• AI Safety in Generative AI Large Language Models: A Survey [14.737084887928408]
  Large Language Model (LLMs) that exhibit generative AI capabilities are facing accelerated adoption and innovation. Generative AI (GAI) inevitably raises concerns about the risks and safety associated with these models. This article provides an up-to-date survey of recent trends in AI safety research of GAI-LLMs from a computer scientist's perspective.
  arXiv  Detail & Related papers  (2024-07-06T09:00:18Z)
• Generative AI and Large Language Models for Cyber Security: All Insights You Need [0.06597195879147556]
  This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs) We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification, cyber threat intelligence, malware detection, and phishing detection. We present an overview of LLM evolution and its current state, focusing on advancements in models such as GPT-4, GPT-3.5, Mixtral-8x7B, BERT, Falcon2, and LLaMA.
  arXiv  Detail & Related papers  (2024-05-21T13:02:27Z)
• 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)
• Mapping LLM Security Landscapes: A Comprehensive Stakeholder Risk Assessment Proposal [0.0]
  We propose a risk assessment process using tools like the risk rating methodology which is used for traditional systems. We conduct scenario analysis to identify potential threat agents and map the dependent system components against vulnerability factors. We also map threats against three key stakeholder groups.
  arXiv  Detail & Related papers  (2024-03-20T05:17:22Z)
• The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning [87.1610740406279]
  White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons. Current evaluations are private, preventing further research into mitigating risk. We publicly release the Weapons of Mass Destruction Proxy benchmark, a dataset of 3,668 multiple-choice questions.
  arXiv  Detail & Related papers  (2024-03-05T18:59:35Z)
• Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science [65.77763092833348]
  Intelligent agents powered by large language models (LLMs) have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines. While their capabilities are promising, these agents also introduce novel vulnerabilities that demand careful consideration for safety. This paper conducts a thorough examination of vulnerabilities in LLM-based agents within scientific domains, shedding light on potential risks associated with their misuse and emphasizing the need for safety measures.
  arXiv  Detail & Related papers  (2024-02-06T18:54:07Z)
• Data Poisoning for In-context Learning [49.77204165250528]
  In-context learning (ICL) has been recognized for its innovative ability to adapt to new tasks. This paper delves into the critical issue of ICL's susceptibility to data poisoning attacks. We introduce ICLPoison, a specialized attacking framework conceived to exploit the learning mechanisms of ICL.
  arXiv  Detail & Related papers  (2024-02-03T14:20:20Z)
• 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)
• Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety [54.478842696269304]
  The use of deep neural networks (DNNs) in safety-critical applications is challenging due to numerous model-inherent shortcomings. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. Our paper addresses both machine learning experts and safety engineers.
  arXiv  Detail & Related papers  (2021-04-29T09:54:54Z)
• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
  This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
  arXiv  Detail & Related papers  (2020-07-05T18:22:40Z)

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.