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Recent Advances in Attack and Defense Approaches of Large Language Models
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Large Language Models (LLMs) have revolutionized artificial intelligence and machine learning through their advanced text processing and generating capabilities. However, their widespread deployment has raised significant safety and reliability concerns. Established vulnerabilities in deep neural networks, coupled with emerging threat models, may compromise security evaluations and create a false sense of security. Given the extensive research in the field of LLM security, we believe that summarizing the current state of affairs will help the research community better understand the present landscape and inform future developments. This paper reviews current research on LLM vulnerabilities and threats, and evaluates the effectiveness of contemporary defense mechanisms. We analyze recent studies on attack vectors and model weaknesses, providing insights into attack mechanisms and the evolving threat landscape. We also examine current defense strategies, highlighting their strengths and limitations. By contrasting advancements in attack and defense methodologies, we identify research gaps and propose future directions to enhance LLM security. Our goal is to advance the understanding of LLM safety challenges and guide the development of more robust security measures.
Forward citations
Cited by 7 Pith papers
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LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems
A systematic review that categorizes LLM threats, severity scores, and mitigations across development and operation life cycles and multiple deployment scenarios.
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CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations
A GAN learns to shift malicious prompts into the safe region of an LLM's internal embedding space, and its discriminator is reused as a no-fine-tuning defense filter.
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MedSentry: Understanding and Mitigating Safety Risks in Medical LLM Multi-Agent Systems
A 5,000-prompt medical safety benchmark reveals that decentralized LLM multi-agent teams resist a malicious insider agent better than shared-pool teams, and a personality-screening defense partially restores safety.
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Mechanistic Exploration of Backdoored Large Language Model Attention Patterns
Backdoored Qwen2.5-3B models show attention deviations concentrated in layers 20-30, with single-token triggers localized and multi-token triggers diffuse.
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Should LLM Safety Be More Than Refusing Harmful Instructions?
LLMs that can decrypt common ciphers show safety failures split across two dimensions, refusing too much or generating unsafe output, and current defenses fix one side while breaking the other.
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Adversarial Prompting Framework for AI Safety Assessment
An adversarial prompt testing framework with a five-level attack taxonomy and a composite harmfulness score is proposed; the paper claims encoded prompts bypass safety filters most often.
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A Survey on Model Extraction Attacks and Defenses for Large Language Models
A taxonomy of model extraction attacks and defenses for large language models, with proposed evaluation metrics and future research directions.
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